Feedback Electroencephalography and Sensory Data Capture

Thomas B. Mulholland

Introduction

This chapter describes the history, applications and speculative interpretation of one kind of feedback electroencephalography, an EEG alpha-contingent stimulation system (ACS) which was developed for research on an EEG component of the human orienting response - alpha blocking - (Mulholland & Runnals, 1961; 1962b; Sokolov, 1963).

A posterior EEG alpha rhythm (8-13 cps) is recordable from 88-96% of adults, in youth (a mature EEG is evident after age 12) and old age, in men and women,and in all ethnic and racial groups. For a comprehensive review of the normal EEG see Lairy (1976). A minority of humans (4-12%) have little or no alpha frequencies in their EEG (Gibbs, et al, 1943; Vogel and Fujiya, 1969) and may include a genetically anomalous sub-group (Anokhin, et al, 1992).

One of the familiar features of a normal, waking posterior EEG is a specific response to sensory stimulation, the classical "alpha-blocking" response. If posterior alpha rhythms (813 cps) are occurring and a sensory stimulus is presented, especially a visual one, alpha frequencies are markedly attenuated and an interval of low amplitude, mixed frequencies is observed. For a serial sample of alphablocking responses, the latency (timedelay between a stimulus ON and the start of alphablocking) is reasonably stable; the timedurations of alphablocking are much more variable.

ACS is a biofeedback method by which stimuli (usually visual) which cause alphablocking are made to be contingent upon the occurrence of EEG alpha frequencies. It is however, an application of biofeedback method different from the more familiar methods for training a person to control the spectrum of their EEG (Kamiya, 1969). Whereas the latter emphasizes an EEG frequency as an emitted response, an operant whose probability can be increased or decreased by proper training, ACS emphasizes the suppression of alpha frequencies following a sensory stimulus as an elicited response, a respondent. A time series of alpha-blocking and alpha segments of an EEG is less variable when it is obtained with ACS compared to other schedules of stimulation and this means increased reliability of that dependent variable.

Interestingly, a feedback EEG experiment was reported early in the history of EEG (Adrian & Matthews, 1934). See Mulholland (1969) for other early examples of feedback EEG.

From the beginning of EEG science, alpha blocking was believed to be associated with a psychological process of attention (Berger 1927). Alpha was somehow linked to lower attention levels while alpha blocking or "low amplitude, fast activity", was linked to higher levels of attention. The "attention" hypothesis proved to be quite robust, seemingly immune to evidence against it (Evans & Mulholland, 1968). Recently, in an incisive review, Shaw (1992, 1994) has challenged traditional assumptions about the origin and function of EEG alpha and alphablocking and their relation to "attention".

At the same time that theories of alpha-blocking in relation to attention were being proposed, new theories about integrative processes in the brain and their relation to perception, attention, and consciousness were developing in neurophysiology. For a recent theoretical discussion, see Jasper, (1991). The question of the neural basis of the human EEG, from the microstructure of cells to cell assemblies is still an active topic in current research and theory. For reviews see Lopes da Silva, (1991) and Steriade, et al, (1990).

Throughout the history of EEG science, studies of alpha and alphablocking were often encumbered by assumptions which were either unproved, or ad hoc, and which were hidden rather than explicitly stated. Many of these assumptions are still extant:

Assumption 1: EEG is "background", "noise", "spontaneous", i.e., a continuous random variable which is statistically stationary. These conventional assumptions are ubiquitous in the literature on the average evoked potential (Pivik, et al, 1993). Experiments have shown however, that they are not defensible assumptions; for instance, the spectral mix of a typical EEG is not statistically stationary for more than 815 seconds. (Praetorius, et al, 1977; Basar, 1990). The occurrence of alpha in the posterior EEG is not necessarily random; durations of sequential alpha and not-alpha segments are not necessarily random; and alpha rhythms are not describable as "noise" as will be shown in this chapter (Mulholland & Goodman, 1980).

Assumption 2: All alpha frequencies are equivalent. This is demonstrably false. There are many alpha frequencies having different cortical origins and recorded from different locations on the scalp which respond differently to stimulation and motor acts (Cooper & MundyCastle, 1960; Walter, 1969, 1950; Lehmann, 1971, 1984; Kuhlman,1978; Steriade, et al, 1990; Gratton, et al, 1992; Michel, et al, 1992). A lumping of alphas into one undifferentiated class "the alpha rhythm" was often found in the early biofeedback and psychophysiological literature. Alpha rhythms in this chapter refer to occipital and occipital parietal alpha rhythms recorded with socalled bipolar electrodes.

Assumption 3: Alpha-blocking can be interpreted as being caused by or associated with increased "attention". The concept of attention is a classical and still important concept in psychology and behavioral science. Its referent is a subjective mental state or a class of behaviors, e.g., orienting responses. Acceptance of the term "attention" as an explanatory concept was part of EEG science from the beginning (Evans & Mulholland 1969). The reality is however, that the term attention has so many meanings it has minimal utility as an explanatory concept in feedback electroencephalography. It is too easy to reify; too difficult to specify. For this reason, the author has adopted a new term which is both neutral and has fewer meanings "sensory-data capture" which, as will be shown later in this Chapter, can be observed and measured. It can be conceptualized as a sub-process which is associated with alpha-blocking in the posterior EEG and which can be mobilized as part of a superordinate attention process.

Assumption 4: Alpha is associated with low arousal, low attention; alpha-blocking is associated with higher arousal, activated attention. This assumption is not necessarily true. Many researchers have shown that alpha can be associated with higher and lower levels of arousal, vigilance, or "attention" (Mulholland & Runnals, 1962a; Kreitman & Shaw, 1965; Mulholland, 1969; Creutzfeldt, 1969; Mulholland & Peper, 1971; Yingling, 1980; Collins, et al, 1990)

Experimental evidence supports the hypothesis that posterior alphablocking is primarily caused by an oculomotor control process, a mobilization of the "triad of accommodation" (Mulholland & Peper 1971; Mulholland, 1969) and a "retinal feedback component of oculomotor activity" (Wertheim 1974, 1981; Shaw, 1992) which are subprocesses, part of a superordinate process of sensorydata capture.

Assumption 5: The alphablocking segment which follows a stimulus is the response, i.e., it is the singular response to a single sensory stimulus. An alternative view however, better supported by experimental evidence, is that an alphablocking response following a sensory stimulus is a disturbance of an ongoing time series of alpha and alpha-blocking segments, a disturbance which persists and decays after the stimulus is removed (Mulholland & Runnals, 1964; Mulholland & Gascon, 1972). Bagchi, (1937) termed this phenomenon "intermittent adaptation". Thus, one stimulus can produce an alteration of time duration of each of a subsequent series of alpha-blocking and alpha segments (Mulholland & Runnals, 1964).

Assumption 6: Within a specific alphablock, all parts of it, the beginning, middle, and end are associated with the same or similar processes. This assumption has been termed the "functional similarity" hypothesis (Mulholland, 1972). An alternative view which can be defended logically but which needs to be examined experimentally, is that from beginning to end of a single alphablock, many different processes occur, cascading and overlapping in time, until alpha returns (Goodman & Mulholland, 1988). Spectral composition however, may remain the same from beginning to end of an alpha-block (Goncharova & Barlow, 1990) although this issue requires further research.

Assumption 7: Alpha segments (alternating with alphablocking segments) in a time series are functionally equivalent with regard to the level of alertness or arousal with which they are associated. Similarly, all alphablocking segments are functionally equivalent. This assumption is perhaps, an oversimplification. For instance, alpha at the beginning of a timeseries following a stimulus may be associated with more alertness than alpha blocking at the end, after several stimuli have been presented (habituation) an hypothesis which needs to be examined further (Mulholland, 1972).

Assumption 8: Alpha and alphablocking are associated with specific opposite psychological states. Certainly these are different EEG states, but it remains to be proved that alpha-blocking is reliably associated with any psychological states (Plotkin, 1976). Alpha is not necessarily associated with an opposite state of decreased arousal or diminished vigilance as compared to that which may be associated with alphablocking. Alpha seems to be associated with a smaller set of states of cortical arousal compared to alpha blocking, i.e., it is usually associated with a middling level of cortical arousal. Thus alpha can occur when a sleepy person becomes more wakeful; or when a vigilant person becomes less alert.

Assumption 9: Various sensory stimuli and different modalities, visual, auditory, tactile, olfactory, etc., are equally effective with regard to producing alpha blocking. It was recognized early in EEG studies that visual stimuli were most effective for eliciting posterior alphablocking (Adrian, 1943). Auditory stimuli were less effective in this regard (Durup & Fessard, 1935). Also, vision is involved in the initial responses to other sensory stimuli, e.g., we look toward the source of a sound, toward the location of a touch, or to the origin of an odor as part of the initial exploration of a new stimulus. If there is no visual component, the alphablocking response to a sensory stimulus habituates quickly (although habituation to painful stimuli is not proved). Alphablocking responses to visual stimuli however, still occur even after many dozens of stimulus repetitions (Sokolov, 1963) though the durations of each is less than earlier ones. Visual stimuli which are signs or symbols can have a different effect on the alpha-blocking process than stimuli qua stimuli (Mulholland & Davis, 1966; Sokolov, 1958).

In sum, research on the classical alphablocking response from 1925 1960 reflected a variety of assumptions and theories. Methods and procedures were not standardized, definitions of psychological processes of attention were inconsistent, and quantification of the response was neither detailed nor specific enough. Researchers disagreed about the variables to be controlled in an experiment and results from different experiments were not always comparable. For this reason, many of the previous findings (or lack of them) may need reevaluation with an improved method.

Alpha-contingent Stimulation: Method and Procedure

Before discussing the specifics of ACS method, there are general methodological requirements for biofeedback and for EEG recording.

Biofeedback systems are sometimes wrongly described as if only a single physiological process were part of the system. The term "alpha feedback" is an example of an oversimplified, misleading designation. A biofeedback system however, always includes a whole person, one who has an identity, beliefs, unique expectations and perceptions, and idiosyncratic ways of relating to and interacting with others. No one comes to biofeedback with just their stated problem or goal. All of the personality traits, prejudices, values and fears developed over a lifetime come along with their EEG. It is a person who has a verbal and non-verbal dialogue with a biofeedback practitioner, who interprets the meaning of the biofeedback display and reacts to it and the setting in which biofeedback training occurs. Without the recognition of a person in the system, biofeedback could be an elaborate but superficial technology.

ACS method is used to study human data-capture which can be altered in unpredictable ways by a participant's emotional reactions to the testing laboratory and its staff. Staff must be carefully selected and trained so that they can, by their skillful interactions with people, detect and minimize anxiety and encourage a positive feeling or attitude. This permits a volunteer to respond to the procedure and the enclosed, dark testing room in a more predictable way with minimum contribution of fears, anxiety, etc., which can distort a typical EEG alphablocking response to stimuli or reduce the amount of alpha too far below that volunteer's typical baseline. This is especially important when children are examined.

Participants should also be given an opportunity to familiarize themselves with the testing procedures and setting, e.g., they can come initially for a "mock" trial. This can reduce anticipatory responses to the experimental setting which can distort responses to ACS (Whittset, Robinson, & Kaplan, 1987).

The method of ACS requires meticulous adherence to the proper procedures of clinical electroencephalography. This means that valid and reliable recordings be obtained which are free of artifact and common kinds of interference before the EEG is passed on to various filtering and signal processing devices. The most common sources of artifacts and interference are signals from the beating heart and contractions of the musculature, movement of the conducting wires, from 60 cycle/sec power lines and from interfaces of silver/silver chloride sensors, conducting gel and scalp. Fortunately there are excellent texts available which describe common artifacts and proper EEG technique (Niedermeyer & Lopes da Silva, 1987; Pivik, et al, 1993).

Alpha contingent stimulation (ACS) was developed to standardize the method for eliciting and quantifying the latency and duration of alphablocking following sensory stimulation and, to control the response by reducing unpredictable variation of the latency and duration of alphablocking. The raw data are time durations of alpha and not-alpha segments in a time series. Data analysis preserves the temporal sequence of EEG segments, i.e., the time-arrow, and in that way complements methods which pool sequential samples of alpha and not-alpha segments (Pfurtscheller & Aranibar, 1977; Pfurtscheller, et al, 1988). The method also facilitates the production of a controlled coincidence of (1) a specific stimulus, (2) a cognitive process, and (3) an EEG segment within a brief time frame.

The ACS method operates as follows. When alpha activity is above a preset threshold of the amount of alpha frequencies in a participant's EEG it is detected by a circuit which then switches on a stimulus. As described before, the stimulus is followed by a blocking or attenuation of the alpha rhythm for a variable time interval or duration. Alpha reoccurs and the sequence is repeated as long as ACS is operating, resulting in consecutive segments of alpha and "not-alpha", i.e., a time-series of EEG segments. See Figure 1.

Figure 1 about here


The linking of EEG alpha segments to the occurrence of a stimulus, an alphacontingent stimulus, was originally accomplished with a rather simple configuration of a Grass Model III EEG machine, vacuum tube filters and amplifiers, relays, and incandescent lamps, slide projector and a shutter controlled by air-core solenoids (Mulholland & Runnals, 1961; Mulholland, 1962, 1969). EEGs were scored by hand by trained clerks who measured the relay markers on the record to the nearest 0.1 sec. (Mulholland & Runnals, 1961, 1962b). The integration of a DEC PDP-12 computer with the ACS system was accomplished in 1970. Software for collecting and measuring time series of alpha and not-alpha durations was developed by Brad Cox (with 4K of memory!) (Mulholland, 1962,1969). A next generation system was designed and built by Boudrot (1972) which was integrated with a DEC PDP-12 computer and an audiometric testing room. All of the software for that system (and subsequent ones) was written and developed by Goodman (1973). A third generation system was used after 1977, (Boudrot, Goodman & Mulholland, 1978). Today, an equivalent system can be configured using microprocessors integrated with LSI circuit boards and off-the-shelf user-friendly software for IO; signal detection, processing and analysis; data quantification; creation of a display; and feedback control of the display. These technological advances have made EEG biofeedback more accessible for clinical applications in the fields of behavioral medicine and education.
The temporal relationships among an EEG, detectors of alpha frequencies, and stimuli are schematically illustrated in Figure 2. t1 is the time delay between recorded EEG alpha exceeding criteria for detection and the alpha detector ON. In the studies discussed here, t1 was about 0.25 sec. t2, the time delay between the alpha detector ON and the stimulus ON was usually negligible. t3 is the time delay between stimulus ON and an EEG response below criterion for alpha detection. This is a biological delay termed, in the jargon of EEG, the latency of alpha blocking. The latency is usually between 0.15 and 0.25 sec. (Cobb, 1963). t4 is the time between alpha-blocking in the EEG and the alpha detector OFF. t5, the time delay between the alpha detector OFF and the stimulus OFF, was negligible in the studies presented here. t6 is the time delay between stimulus OFF and the next alpha detector ON. This is termed the duration of alpha blocking, when t5 is 0.

[Insert Figure 2 about here]


Whatever specific technology, is utilized, the EEG segment defined as "alpha" must be in a specific frequency band (812 cps), have sufficient power (>25 per cent of maximum recorded with eyes closed) and last long enough (0.20.3 secs) so that an alpha segment is formed and detected and, a stimulus can be presented. Stimulus ONOFF delays following the alpha detector should be settable. Usually, the ON delay of the alpha detector relative to onset of an EEG segment over threshold for alpha detection was about 0.2 seconds. Recently, an improved alpha detector allows minimum ON and OFF delays of the alpha detector of <0.1 sec. (Kodama & Mulholland, 1992). Experiments have shown that these values of filter frequency, rms power threshold, alpha detector/stimulus delay so act that during feedback stimulation, the variability of alpha and notalpha segments is minimized, i.e., the system is properly "tuned" (Mulholland, Boudrot, & Davidson 1979). Ideally, each participant's ACS system would be individually "tuned" so that under ACS conditions, random variability of alpha and notalpha durations would be lowest, i.e., replicative reliability would be highest. See Figure 3 for a flow diagram of a typical system.

[Insert Figure 3 about here]

Two different EEG-stimulus contingencies have been used in application of ACS method termed Loop 1 and Loop 2:a configuration where a stimulus is contingent upon alpha is called Loop 1; a reverse configuration where a stimulus is contingent upon notalpha is called Loop 2. Usually, Loop 1 acts like a timedelayed, quasi-negative feedback. Loop 2 performs like a time-delayed, quasipositive feedback (Mulholland, 1968). Most of the research reported here was done with Loop 1. A third kind of schedule of stimulation which was non-contingent, was termed "sham". It was a schedule of stimulation which was that obtained from a different participant under ACS conditions or, a schedule which was created to have similar interstimulus interval and occupy about the same total time, etc. A random schedule was not used.

All experiments discussed here were done using an audiometric testing room (8 X 12 ft) which minimized ambient visual and auditory stimulation. Comfortable seating facilitated a relaxed posture and minimal movement. Continuous monitoring of behavior and EEG of the volunteer ensured that excessive movement, level of wakefulness or sleep and EEG artifacts could be observed. Continuous monitoring can be accomplished by TV cameras in the soundreduced room and with online, strip chart or videographic displays of the EEG.

In most experiments, visual stimuli were produced by photographic transparencies and a projector with an electronically controlled shutter. Alpha detector ON causes stimulus ON; alpha detector OFF causes stimulus OFF. Any type of visual stimulus can be presented or illuminated contingent on the detection of alpha, e.g., words, text, photographic transparencies, TV displays, computer graphics, and even human volunteers who, when illuminated in the dark by an alpha-contingent light, become a social stimulus (Mulholland, McLaughlin & Benson, 1979).

Sometimes, alphacontingent stimulation may not be a feedback. For instance, the person viewing the contingent stimulus may change his/her way of capturing sensory data, e.g., the volunteer may not look at the stimulus or may try to capture data from other stimuli such as sounds or an audible message. When this happens, a person viewing ACS may go "out of the loop". Then the system is not a biofeedback system and becomes "dis-regulated" (Schwartz, 1972; Mulholland, et al, 1983). The fact of error correcting feedback can not be deduced from the fact of contingency; rather it must be demonstrated by the kind of control which is achieved (Mulholland, 1977).


The procedure for evaluating the EEG during an experiment was standardized for most applications. A participant's EEG was recorded with eyes closed until alpha rhythms were clearly evident in EEG. The alpha detector's threshold was set and the system was operated without presenting any stimulus until about 10 alpha and 10 not alpha segments had been collected. Then the participant was asked to open his/her eyes and the system continued operating without presenting a stimulus until about 10 alpha and not alpha segments were recorded. After these "eyes closed" and "eyes open" baselines, ACS and "sham" feedback conditions were evaluated. In each of these conditions 30 alpha and 30 not-alpha segments were recorded. Sham feedback was a schedule of visual stimulation which was not contingent upon the alpha rhythm. The stimulus durations and the interval between stimulations were similar to those obtained with ACS stimulation.
An experimental trial or condition was defined by 10 alpha and necessarily 10 not- alpha segments with no stimulus and with eyes open in the dark (Before feedback) continuing without an interruption into ACS or sham feedback until 30 alpha and 30 not alpha segments were collected (During feedback). An experimental session was made up of several trials. At the end of the session, eyes opened and eyes closed baselines were recorded again.

When research on the ACS method began (Mulholland & Runnals, 1961) a consensus of EEG research literature held that alpha rhythms were abundant with eyes closed in the dark, that alpha rhythms were blocked initially when eyes were opened in the dark, and that alpha-blocking followed a visual stimulus though there were exceptions (Shaw, 1992). It seemed reasonable then that ACS would be best done with eyes closed, i.e., stimulating through closed eyelids. Subsequent experiments showed however, that copious alpha could be recorded from volunteers whose eyes were open in the dark after a few minutes (Mulholland 1965); moreover, alpha-blocking following ACS was always followed by a return of alpha when the visual stimulus was removed (Sokolov, 1958).

With eyes open, visual stimuli which were especially effective for eliciting data-capture responses, e.g., images of sexual or aggressive behavior and meaningful verbal messages or words, could be presented. This permitted studies of cognition and language not possible if eyes were closed. An incidental benefit was also found with eyes open, participants didn't fall asleep so readily! All subsequent studies after 1965 were done with eyes open; eyes closed conditions were used primarily for baselines and calibration.

The method of alphacontingent stimulation for evaluating sensory data-capture has been used primarily with visual language and non-language stimuli. EEG recordings are usually bilateral, most often from occipital and occipitalparietal locations, O1, O2, O1P3, O2P4 in the standard nomenclature for locating probes on the scalp (Jasper, 1958; Niedermeyer & Lopes da Silva, 1987). Because alpha frequencies recorded from other locations on the scalp are associated with different neurophysiological and behavioral processes (Kuhlman, 1978), the results discussed here cannot be generalized to other locations and refer only to occipital or occipitalparietal, so-called bipolar EEGs unless indicated otherwise.

When these methods and procedures are used, valid, reliable recordings under ACS conditions can be obtained from most children as young as 5 or 6 years old (Mulholland & Gascon, 1972; Bryan, 1982) and from most adults up to 75 or more years old, and from patients who are diagnosed with neurological and psychiatric illness (Mulholland, et al, 1976, 1979; McLaughlin, et al, 1974; Bundzen, 1966; McLaughin & Koff, 1989). Obviously, ACS cannot be used with those EEGs which contain little or no alpha but, as described before, these are a minority of normal EEGs, from 4-12%.


Data Collection And Quantification

For the purposes of the ACS method, the waking EEG can be dichotomized as a series of alpha segments alternating with notalpha segments. The series of alpha and not-alpha segments are numbered beginning with the onset of an experimental condition. Thus N1a is the first alpha segment detected; N2a the second, etc. Similarly, N1na is the first not-alpha segment detected; N2na is the second, etc. In most of the studies presented here, N during ACS was 30 for both alpha and for not-alpha segments preceded by 10 alpha and 10 not alpha segments collected without ACS. These "before" and "during feedback" samples of EEG segments defined a trial.

It can be seen in Figure 2 that the duration of an EEG alpha segment is approximately equal to t1 ON delay + t3 (latency) - t4 OFF delay and that the duration of EEG alpha blocking is approximately equal to t4 OFF delay + t6 - t1 ON delay, when t2 and t5 are negligible. Time durations of alpha detector ON and OFF were good approximations to durations of EEG alpha segments and of not-alpha segments because alpha detector ON and OFF delays are approximately equal.

The successive time durations of alpha and not-alpha segments were collected and displayed on-line as computer graphics as two separate samples. Usually, this was done for both the so-called connected EEG and the contralateral homologous EEG which was not connected to the feedback system.

As described previously, alpha and not-alpha segments occur alternately in a trial. Bestfit functions computed in each trial were used to estimate the trend of alpha durations and separately, of notalpha durations as a function of the ordinal number of the EEG segment both before and during feedback.

These functions, described below, were empirically selected and were not derived from any particular theory. Other polynomials were fitted and some fitted equally well; without a theoretical justification the choice of a particular polynomial is arbitrary. Estimates of trends from each trial are pooled to obtain average bestfit functions of latency and duration of alphablocking. In the computer graphics and in some of the illustrations in this chapter, the trends were plotted as a continuous function; however, values of the function exist only for the ordinal numbers of the segments in a series.


f1(N) t alpha= AN + B ± Se

t is time duration of the alpha detector ON; N is the ordinal number of the alpha segment in the time series; Se is the standard error of estimate; A, B are constants.

f2(N) t notalpha = C/N + D + Se

t is time duration of the alpha detector OFF; N is the ordinal number of the notalpha segment in the time series; Se is the standard error of estimate; C, D are constants.

From these functions and related statistics, other statistics can be computed:
(1) % time alpha = t alpha/( t alpha + t not-alpha)

(2) Total time in trial (T) = ( t alpha + t not-alpha) x N

(3) Index of orienting (IO) = t not-alpha/ t alpha

(4) Latency of alpha blocking = t alpha - OFF delay.

Corrections for baseline can be made by subtracting B and D,
computed for segments before feedback from f1(N)alpha and
f2(N)not-alpha, respectively. Also see Mulholland (1984).
Software for collecting, displaying, storing, and analyzing the samples of t alpha and t not-alpha were developed by David Goodman (Goodman, 1973; Goodman, et al, 1980; Boudrot et al, 1978).


Applications
Applications of the method by the author which have already been described will be discussed briefly. Other applications however, some speculative and as yet untried, are proposed here. When some of these applications were first suggested (Mulholland, 1970, 1973, 1978) implementation was limited by the lack of a suitable and accessible technology. Fortunately, no such limitation exists today, so it makes sense to discuss them here. Several studies of ACS were "first of a kind" and it will be obvious that more research is needed to extend them.
ACS was developed as a research method and applications of it are in the following categories: (1) standardized quantification of the elicitation and habituation of the alphablocking response; (2) modification of the habituation process online by linking cognitive processes to specific alphacontingent stimuli and specific EEG segments within a brief time frame; (3) monitor and quantification of inhibition of alphablocking; (4) evaluation of disregulation of the alpha-blocking time series, e.g., when ACS is not an errorcorrecting feedback; (5) clinical evaluation: (a) evaluation of reactivity of the EEG to sensory stimulation; (b) bilateral alpha symmetry of the EEG during ACS and cognitive processes; (c) topography of alpha-blocking; (6) testing hypotheses about the existence of an internal, forward path from receptor to brain processes to EEG.

ACS method with most subjects who have recordable alpha has a readily recognizable signature an alternation of alpha "bursts" (lasting less than one second) and intervals of alphablocking in the EEG recorded from both left and right sides. The regularity of this alternation of alpha and not alpha segments is observable on the recording, i.e., variability is less than the usual resting EEG record which belies assertions that the EEG is a "background" to the stimulation process. Several experiments show that ACS (Loop 1) reduces unpredictable variability of timedurations of alpha and notalpha segments as compared to not-alpha contingent stimulation (Loop 2), or "sham feedback", or eyes-open baseline, or other schedules of stimulation. (Mulholland & Runnals, 1962b; Mulholland, 1968; Peper, 1970; Mulholland & Peper, 1971; Eberlin & Mulholland 1976; Mulholland & Eberlin, 1977; Mulholland, Boudrot & Davidson, 1979; Goodman, Beatty & Mulholland, 1980; Mulholland & Goodman, 1982; Mulholland, Goodman & Boudrot, 1983; Goodman & Mulholland, 1988; Mulholland, 1990).

This effect is reliable and useful because, as is well understood, the smaller the sample variance, the smaller the number of samples needed to achieve an equally good estimate of central tendency, trend, and significance of the differences between Means. This reduces sample sizes, sampling time, and cost. The source of the greatest variance in experiments with ACS is from differences among individuals.

The unpredictable variation of alpha timedurations and notalpha timedurations is reduced bilaterally but is reduced more on one side compared to the other. Many experiments have shown that the unpredictable variation of alpha and notalpha segments is less for the EEG connected to the stimulus by the external path of electronic detection and feedback apparatus (connected EEG) compared to the homologous, contralateral EEG which is recorded but is not connected by that external path (not connected EEG). Also, alpha durations are better controlled (Mean/Se) than notalpha durations in the EEG connected to the stimulus by an external path compared to the EEG not connected (Eberlin & Mulholland, 1976; Mulholland & Eberlin, 1977; Mulholland, 1977; Goodman, et al, 1980; Mulholland & Goodman, 1982; Mulholland, 1990). The term "connected EEG" emphasizes the fact that under some conditions (participant falling asleep or engaged in capturing other sensory data) an alpha-contingent stimulus is not necessarily an error-correcting feedback and the alpha and not alpha segments are not controlled by the contingent stimulus.
Also, recordings were usually obtained from a contralateral, homologous EEG in addition to the "connected" EEG. Despite the neural connections between the hemispheres, there is a slightly less reliable contingency between "non-connected" EEG and the stimulus. An alpha-stimulus contingency for a non-connected EEG can be more or less reliable depending on the similarity of the not-connected EEG compared to the connected EEG.

A typical example of an EEG recorded under ACS conditions is shown in Figure 1. The recordings in the tracing are bilateral, bipolar EEGs from posterior sites O1OI and O2OII. OI and OII are locations between O1-P3 and O2-P4, respectively. In between the tracings are markers which identify the alpha detector ON and OFF. The marker is above its center line for alpha in the top tracing and below its center line for alpha in the bottom tracing. Also a onesecond time marker is shown. Calibration is 50 microvolts/one second. The top set of tracings are from baseline; alpha detectors ON but no stimulus is presented. The bottom set is ACS; alpha detectors ON and simultaneously a stimulus is presented.

Standardized quantification of elicitation and habituation of alpha-blocking.

The timeseries of durations of alpha and notalpha segments, measured under ACS conditions, changes as a function of the number of successive, repeated (redundant) stimuli. This is the classical habituation of the alphablocking response (Sharpless and Jasper, 1956). Habituation as described by pooled averaged data is usually monotonic; an individual experimental trial however, may reveal systematic, or random or quasirandom variation (Morrell & Morrell, 1962; Mulholland, 1964).
Habituation of the response is defined by the best fit functions: alpha durations (f1N) which is an estimate of the latency of alpha blocking and not-alpha (f2N) an estimate of duration. Alpha blocking when fully habituated has a duration equal to the asymtote of the function of not-alpha durations. This value is arbitrarily close to the value of the function at N=30.


When data from a single trial are pooled with other trials or other individuals, individual data points (N1, N2, N3, etc....)can be pooled and a best-fit function computed for those average data - a function of the average. Alternatively, the best-fit functions computed in each trial can be pooled - the average of the functions. The two estimates converge to the same function as the sample size increases; in the research reported after 1965, the latter procedure was used. ANOVA was done for the samples of the variables and parameters of the best- fit functions, A, B, C, D, Mean, SE, Mean/SE, etc.

The best-fit functions can be interpreted in terms of a two- process theory of habituation of alpha blocking - a phasic and a tonic component. The phasic component is brief and does not habituate and the time of arrival of an EEG sign of the phasic component is sooner than for the tonic process. The tonic process has a longer duration, habituates with repeated stimulation and its time of arrival in the EEG is later than for the phasic process (Sharpless & Jasper, 1956). There is no clear sign of the arrival of a tonic process in an EEG alpha-block recorded from one scalp location;it may overlap in time the earlier phasic process. Moreover, several sub-processes each with a different arrival time and overlapping in time may be included in the overall alpha-blocking process. Following an initial stimulus, alpha-blocking may be recorded from many locations; as stimulation is repeated and habituation occurs, alpha-blocking following the habituated visual stimulus becomes localized in the posterior locations.

Both the latency and the duration of a phasic process can be estimated from an EEG recorded from a single site. Since the phasic process arrives in the EEG first, the latency of the alpha blocking response is the latency of the phasic component. Since latency is included in the duration of t alpha as described previously, the alpha best-fit function is the phasic latency function during ACS.

The duration of the phasic process is included in t not-alpha. Since the phasic process does not habituate, its duration can be estimated when the tonic component has been habituated after 30 repetitions of the stimulus during ACS (Sharpless & Jasper, 1956). This estimate is the asymtote of the not-alpha best-fit function. The not-alpha best-fit function is a function of the tonic and phasic duration in combination. Because the phasic process is brief and does not habituate, the not-alpha best-fit function is mainly an estimate of tonic processes for the first 10-15 repetitions of the stimulus during ACS.


This standardized, quantitative, reliable estimate of habituation of alphablocking permits a more detailed comparison of individuals and groups with regard to the latency of the phasic component and the duration of the phasic and tonic components of alphablocking during ACS.

A study of children illustrates this approach. There were two age groups: 56 years and 910 years. Different habituation functions were found for younger compared to older children under ACS conditions and for alphablocking when they opened their eyes in the dark. The older children produced longer notalpha durations at the beginning of the eyes open condition and for the alphacontingent stimulation condition compared to the younger children. No differences between the age groups were found for the baseline of eyes closed in the dark (Bryan, 1982).

During ACS, the duration of the phasic component of alpha blocking was briefer for the younger children and the duration of the tonic component was initially much longer for the older children and habituated with repetition. When eyes were opened in the dark the older children showed the "intermittent adaptation" described previously by Bagchi (1937), i.e., an initial disturbance followed by a recovery of the series of alpha and not-alpha segments.The younger children showed an initial small increase of not-alpha durations which did not recover. The results also illustrate the methodological point that differences between the two age groups were not evident with eyes closed; only when there was some brain work to do were differences evident.

These results and others (Mulholland & Gascon, 1972) indicate that sensory datacapture may continue to develop during the ages of 510 years. The ontogenesis of the process of sensory datacapture with regard to EEG effects however, remains a neglected topic. See Figure 4.

[Insert Figure 4 here]

Several studies comparing different groups of normals and patients with regard to elicitation, habituation and dishabituation of alphablocking under ACS conditions have been reported: normals and psychiatric and neurological patients (Mulholland, McLaughlin and Benson, 1976, 1979; McLaughlin, et al 1974); patients who were active in a prior interview compared to those who were not (McLaughlin and Lewis, 1975); patients who were depressed compared to normals (Danesino, et al, 1982); and patients with hepatic encephalopathy (McLaughlin and Koff,1989). These studies show that further applications of ACS in clinical research is likely to yield fruitful results; more clinical research is needed. For the comparison of different groups of normals and patients cited above, significant differences among them were found for estimates of the tonic component of EEG arousal; estimates of the phasic component were only minimally different.

Another application of ACS is to study the differences between different kinds of stimuli with regard to the evocation of sensory datacapture and habituation of the alphablocking response. In a representative experiment (Mulholland & Davis 1966) three sets of words which were the contingent visual stimuli were compared: "kotex", "raped", "bitch", "penis".
These were assumed to have meanings which would elicit longer lasting data-capture episodes compared to "dance", "child", "broom" "glass". A third set were scrambled versions of the words in the other sets "lhidc", "pdaer", "caend", "hbtic". The words were presented 30 times in an ACS trial. The sequence of scrambled and nonscrambled words was statistically balanced according to the experimental design. Ten volunteers (five men, five women) were tested.

This experiment showed that reliable differences among individual words and classes of words could be detected despite habituation. This affirmed Sokolov's (1958) observation that alpha blocking following a visual stimulus is a robust response which can be observed even though other components of an orienting response have dropped out after many repetitions of a stimulus. These studies demonstrate that ACS method could be fruitfully applied to study the differences among many kinds of data, e.g., verbal visual language and nonverbal visual language (Randhawa & Coffman, 1978; Mulholland, 1974; Mulholland and Runnals, 1963), even a "real" person stimulus with regard to the process of sensory datacapture (Mulholland, et al, 1979). In these studies, significant differences were obtained for estimates of the tonic component of EEG arousal; minimal differences were found for estimates of the phasic component.

Habituation and covert cognitive processes.

An early application of ACS method was to study what the author then termed "internal attention gradients", i.e., covert cognitive processes shaped by prior verbal instructions (Mulholland, 1962). To study the effect of cognitive processes on sensory datacapture using an EEG method, it is necessary that a (1) covert cognitive process, (2) specific EEG segment, and (3) specific stimulus so be coordinated that they occur within the same brief timeframe. This can be accomplished by verbal instructions which target a particular future stimulus so that it would be a sign to start a cognitive process. Since the targeted stimulus will be contingent with a particular EEG segment, that EEG segment can be identified. Using this procedure, several experiments showed that subjective mental processes could modify, online, the process of sensory datacapture and indirectly, a time series of alpha and notalpha segments.

In one experiment (Mulholland & Runnals, 1963) normal volunteers were tested with four different schedules of sensory datacapture, schedules which were preprogrammed by verbal instructions. Four different schedules were evaluated:
(1) Viewing visual stimuli without counting them; (2) counting each stimulus up to the 15th and not counting the remaining ones; (3) counting all of the stimuli; (4) counting to the 15th stimulus and making a covert judgment whether it were brighter or dimmer than the previous stimuli and, not counting the remaining stimuli. All the stimuli were equally intense; all counting and judgment tasks were to be done silently.

For the schedule of subjectively counting each stimulus up to the 15th stimulus, the average duration of notalpha segments was longer than an average alpha segment for segments 110. For segments 1115 they were about the same. For segments 1630 average alpha segments were longer than average not alpha segments.

For the schedule of counting each stimulus up to the 30th, average notalpha segments were longer than average alpha segments for the entire time series. The magnitude of this difference decreased, however, from beginning to end of the condition.

For the schedule of counting up to the 15th stimulus and judging if the 15th were brighter or dimmer than previous stimuli, average notalpha segments were greater than average alphasegments for segments 112. For segments 1314 the durations of notalpha increased so that the longer duration was the 14th notalpha segment. This was the interval of no stimulus just before the 15th stimulus. After the 15th stimulus, not-alpha durations decreased again.

This kind of experiment has been repeated several times, and demonstrates that ACS method provides an acceptable solution to a difficult methodological problem of creating a controllable coincidence of a cognitive task, an EEG segment, and a stimulus event (Mulholland & Runnals, 1962b, 1963; Mulholland, 1962, 1968, 1973, 1984; Peper, 1970).

In these experiments, the effects of covert counting of stimuli and of "paying attention" to them were evident for estimates of both tonic and phasic components of EEG arousal. Usually, counting the alpha contingent stimuli was associated with a decrease of the latency and of the variability of the latency of the phasic component as well as increasing the duration of the tonic component. In early experiments, ACS was delivered through closed eyelids. With repetition of a visual stimulus, a gradual increase of the estimate of the latency of the phasic component and a decrease of the duration of the estimate of the tonic component was found (Mulholland & Runnals, 1964). In later studies however, when the repeated stimulus was delivered with eyes open, no change in the estimate of the latency of the phasic component was found.

Inhibition of alpha-blocking.

The EEG literature contains hundreds of reports of the elicitation of alphablocking; reports of inhibition of alpha
blocking following stimulation are rare. There have been reports of absence of response due to profound habituation or a lack of response due to poor sensory reception or alteration of sensory processes. These kinds of variables need to be excluded in order to study inhibition of alpha-blocking per se. The method requires measurement of temporal relationships among recorded alpha rhythms, detectors of alpha frequencies, and a control of timing of stimulus presentation in relation to the detection of alpha.

An evaluation of inhibition of an alpha blocking response (and by implication, inhibition of sensory datacapture), is basically a comparison between the response to a contingent stimulus having a brief time delay relative to the onset of alpha compared to a contingent stimulus having a longer delay. As time-delay increases, the mean duration of alpha segments increases; variability (Se) increases; control (X/Se) of alpha durations decreases; and the ratio of the number of alpha segments associated with a stimulus to the total number of alpha segments detected (Ns/Nt) decreases (Mulholland, 1968, 1973, 1979a; Mulholland, et al, 1978).

For evaluation of inhibition of alpha blocking, two alpha contingent stimuli, one delayed relative to the other, are presented. Specifically, a comparison is made between the response to a stimulus having a standard brief delay with the stimulus having a longer delay. If no alpha block follows the first ACS, alpha detector will remain ON. Then the second stimulus is presented. If a response occurs to that ACS stimulus then the response to the first has been inhibited. If however, an alpha blocking response to the first stimulus occurs, the response to the first stimulus has not been inhibited, the alpha detector goes to OFF, and there is no presentation of a second stimulus.

Five normal participants demonstrated various examples of voluntary inhibition of alpha blocking following a visual ACS. (Mulholland, 1979a). Figure 5 shows a bilateral EEG from a participant who can either respond to the first ACS or to inhibit the response to the first but to respond to a second, delayed ACS.

[Figure 5 about here]


All five subjects demonstrated improved control of inhibition of alpha blocking after varying amounts of practice. Three showed good control; two showed less control. The presence or absence of control of inhibition could be appraised easily by monitoring the computer display of the on-line collection of a time series of durations of alpha segments. The results of this initial study indicates that some persons can voluntarily inhibit the onset of alpha blocking (cortical activation).
Disregulation of Controlled EEG

As described previously, when ACS is an "error correcting" feedback the unpredictable variation of the time series of alpha and notalpha durations is reduced. This variance-reducing effect is itself a marker of whether a "forward path", which is a process of the individual person "in the loop", is operating according to expectations. For instance, if a volunteer does not look at the feedback stimulus or directs his/her sensory datacapture toward auditory stimuli or to another visual stimulus which is not contingent, he/she may "go out of the loop", i.e., the EEG becomes "disregulated" (Schwartz, 1979; Mulholland, et al 1983) and variability of the durations of alpha and notalpha segments should increase. When the volunteer captures another, distracting, stimulus rather than the alpha contingent one, that change could be detected by monitoring the variability (Se) or control (X/Se) of the time durations of alpha and not-alpha segments.

Two experiments with normal adults of both sexes tested the
hypothesis that distraction by non-contingent stimuli is associated with increased variability of those neurophysiological
processes that had been regulated by the contingent stimulation. Dependent variables were time durations of alpha segments and
of notalpha segments, the mean rms power in the alpha band of alpha and not-alpha segments and the unpredictable variability of EEG segments (Se) and control (X/Se) of the durations of alpha and not-alpha segments. Independent variables were a combination of counting tasks and instructions to look at, listen to, and count the feedback stimulus, and/or non-contingent visual flashes and audible clicks.

Capture of the feedback data was challenged by instructions to count other noncontingent stimuli. Control (Mean/Se) of alpha and notalpha segments was least for conditions of (1) "sham" (noncontingent) stimulation, and (2) distraction, i.e., ACS (visual) with instructions to count covertly noncontingent auditory clicks. These results indicate that a monitor of sensory datacapture and distraction from the alpha contingent stimulus by noncontingent stimuli (which could include verbal messages with various meanings or urgency) could be developed with more research. (Mulholland, et al, 1983).

A caveat for biofeedback practitioners: contingent feedback stimulation may not be effective in producing self-regulation if there is no forward path or, a very unreliable path, from the stimulus through an error-correction process to the target organ or tissue. Contingent stimulation is not necessarily a negative feedback.

Clinical Electroencephalography.

No application of ACS to clinical EEG evaluations by clinical electroencephalographers has been reported. A few studies indicate that there could be a role for ACS: (1) to evaluate reactivity, i.e., latency and duration of alphablocking and its habituation as a marker for sensory datacapture and variability of the latency in duration blocking; (2) to monitor bilateral symmetry of individual alpha-blocking responses to ACS and of habituation over a series of blocking responses and (3) to identify a topographic pattern of reactivity, bilateral symmetry, and variability of the alpha-blocking response to ACS.

Reactivity. Alpha-blocking in the posterior EEG can be compromised by pathological processes which adversely affect any of a large number of processes which are involved in normal data-capture and the mobilization of data-capture in the service of a superordinate attentional processes. At this level, estimates of abnormal data-capture and habituation with ACS is capable of defining symptoms and identifying functional deficits. Previous researchers had shown that reactivity, i.e., duration of alpha-blocking, was less for a group of "brain-damaged" patients compared to other psychiatric patients and non-patients, e.g., Holloway and Parsons (1971). These findings were confirmed with an improved quantification with ACS method (Mulholland, et al, 1976, 1979).

For those diseases or deficits where a disorder of "attention" is a salient symptom, diminished or disturbed reactivity of the bilateral posterior EEG vis a vis alpha-blocking would be expected, e.g., in narcolepsy, sub-clinical hepatic encephalopathy, and depression. There is also a possible application to study the changes in EEG vigilance produced by anti-psychotic and tranquilizing medications (Ulrich, G., et al, 1990).

In a small study of sub-clinical hepatic encephalography, patients showed shorter durations of alpha blocking compared to a control group. ACS method made a better discrimination between patients and controls than did a psychological test (Trail Making) (Mclaughlin & Koff, 1989). More development is needed for this kind of an ACS application.

A single case study of a narcoleptic demonstrated deficits of sensory data-capture: (1) rapid and profound habituation of his alpha-blocking response to visual stimuli which persisted over successive trials - see Figure 6; (2) an atypically high degree

of control (Mean/Se) of the latency and duration of alpha-blocking response by ACS; (3) disregulation of his response while viewing after-images which was different from disregulation while he was drowsy (Mulholland & Marcus, 1973). This kind of study points to an application of ACS method to the study and diagnostic testing of disorders of vigilance or impaired
wakefulness.

[Figure 6 about here]

Bilateral Symmetry. ACS forces the occipital-parietal EEG into a temporal pattern of brief alpha segments alternating with longer not-alpha segments so that deviations from normal symmetry of waking alpha and response to sensory stimulation which occur moment-to-moment and segment-to-segment, are easily appraised visually, on-line, and readily quantified. The method also provides tests of symmetry during alternating intervals of brain work and nil-work (Adrian, 1943). Obtained frequency of occurrence of bilateral pairs of EEG segments, similar or dissimilar,can be compared with hypothetical expected frequencies with regard to the statistical significance of a difference between expected and obtained frequencies.

In a study of adult men and women, bilateral EEG segments were classified into four categories: alpha L,R; alpha L, not-alpha R; not-alpha L, alpha R; not-alpha L,R. Table 1 presents the total percent of bilateral pairs in each category. Table 2 presents the percent of total time associated with each category.


TABLE 1 *

Percentage of total N of events in each category
Pooled Conditions Categories of Event Pairs

aL, aR aL, naR naL, aR naL, na R
Eyes closed 42.2 7.4 8.4 41.9
Eyes