Biofeedback Research in Japan
by Akihiro Yagi and Fumio Yamada
A. The purpose of the chapter:
More than thirty years have passed since biofeedback research started in Japan. A great number of biofeedback studies has beenreported in the fields of psychology, medicine and engineering inJapan. However, most of them were presented at meetings inJapan and were published in Japanese. Accordingly, the purpose of this chapter isto introduce the biofeedback studies in Japan that are not well knownin foreign countries. Since the majority of biofeedback studies of the lambda waveand the Fm-theta wave of EEG are investigated in Japan, theyare reviewed in detail in special sections.
B. The dawn of EEG biofeedback research in Japan:
Kakigi (1966) conducted the first biofeedback experiment in Japan inthe Kwansei Gakuin University laboratory in 1966. In theexperiment (Kakigi, 1966), the vasomotor response was fed back by anaudio signal (Yagi, co-author of this chapter, has the distinction ofhaving served as the first participant in this experiment while an undergraduate student in the department). In the late 1960’s,many other researches became interested in biofeedback in otherlaboratories in Japan. Miyata and Hamano (1967) published the firstreview on biofeedback in Japan.
Stimulated by Kamiya (1968), EEG biofeedback research inJapan began in about 1970. After Kasamatsu and Hirai (1969) reportedon variations of the alpha and theta wave during meditations ofZen masters in Japan, the study of relaxation training started in theclinical field by the utilization and application of the alpha wave.
At the first meeting of the Japanese Society of Biofeedback Researchin 1973, Yamanaka introduced the study of biofeedback training of thealpha wave that he conducted with Mizuguchi in the National Cancer CenterHospital. Mizuguchi, Yamanaka and Hirai (1974) reported alpha training for the relief of severe cancer pain. After theirresearch, the number of reports on EEG biofeedback graduallyincreased in the fields of psychology, medicine and engineering. Mostof the research on the alpha wave dealt with relaxation training.Because Shirakura and his group summarized and reviewed some of thestudies in Current Biofeedback Research in Japan (1992), we will notdescribe them in detail here.
C. EEG biofeedback systems:
One of the characteristic features of biofeedback research inJapan is research in engineering. The work is divided into two fields.The first field deals with the development of new biofeedback systems while the other consists ofresearch in control theory (Fukumoto, 1979). In the early days of biofeedback research in Japan, no specific machine for alpha wave biofeedback existed. Researchersmeasured EEG with an EEG machine for clinical diagnosis, and when they visuallyobserved the alpha rhythm in the record, they gave feedback signals with a hand-operated switch.
Many types of biofeedback machines were already on sale inthe U.S. However, some of the devices that employed a simple band-pass filter toselect the alpha wave were flawed and permitted artifacts from muscle potentials (EMG)and harmonics of noise to pass through as a result of electrode slippage. When apatient is trained with such a device, he or she may learn tension enhancement instead of tension reduction. Consequently, Yagi and colleagues developed a new device (Fig. 1) and obtained U.S. and Japanese patents on the machine.
The bio-potentials obtained from electrodes on the occipital and the frontal sites are divided into the alpha component (8-13 Hz) and thehigh frequency component (13-200 Hz), along with two filters. The alphacomponent is transformed into a low-pitch tone (800 Hz) and the highfrequency component into a high-pitch tone (1500 Hz) by means of amplitudemodulation. These tones are fed back to the left and right ears,respectively. The timbre of the low tone is like the pleasant song ofthe evening cicada, which conveys the feeling of being in a Zen temple.
----------FIG. 1 YAGI----------
The feedback of the two signals made it possible for participants to distinguish between thetrue alpha wave and the pseudo alpha component. During relaxationtraining, increasing the pure alpha wave and decreasing the high frequency component, including muscle potentials, helped produce effective results. Many hospitals and research laboratories began using the device, which was put on sale by certain companies.
Ohsuga and her colleagues, researchers in the Mitsubishi Electric Co. laboratory, also developed several types ofbiofeedback devices. Recently, they reviewed some of the systems inEnglish (Ohsuga, Terashita & Shimono, 1992). One of their aims was todesign a biofeedback system that motivates patients to learn relaxationor to learn some behavior. Ohsuga pointed out that the presentation of the feedback signal itself could sometimes have an adverse effect on relaxation training.In order to minimize this effect, Ohsuga et al. (1992) developed a machine that transformed the theta, alpha and beta components into three tones, each with a different pitch. A lowtone indicated theta, a middle tone signified alpha and a hightone represented beta. One unit of the feedback signal waspresented as a chain of three different tones with a total duration of onesecond. The ratio of theamplitudes of EEG activities in each band (theta, alpha and beta) determined the duration of each tone. Thetones of the unit were fed back repeatedly during training. Consequently, the participant was able to recognize a change in thedistribution of the three components on account of the rhythm of the feedbacksounds.
The two devices mentioned above were unique to Japan. Many othertypes of machine besides the two examples have been developed forbiofeedback of the alpha wave (e.g., Furumitsu, Itoh, Hata &Kakigi, 1987).
II. Biofeedback of the Lambda Wave
A. Lambda waves and lambda response:
Eye movement recordsshow that when a participant observes a stimulus object, a step-like pattern consisting of saccades and fixation pauses occurs.Some participants show spike-like waves (lambda waves) following thesaccades in the occipital EEG (Evans, 1952). Similar potentials,referred to as lambda responses, are found in almost allparticipants if the many EEGs are averaged, time-locked to saccades (Fig. 2). Wefound that the lambda wave and lambda response were associated with asaccade’s offset and not its onset (Yagi, 1979). The offset of the saccade is the onset of the next fixation pause. Therefore, EEGs should beaveraged at the onset of fixations in order to obtain the lambdaresponse. When EEGs are averaged at the onset of eye fixations,complex potentials can be obtained which consist of many components fromearly to late, like the event-related potentials. We use the term "eyefixation related potential" (EFRP). The lambda response is the mostprominent element in early components of EFRP. In this chapter, thelambda wave and the lambda response are distinguished as follows: thelambda wave is observed in the routine EEG, while the lambda responseis an averaged potential, obtained with the onset of eye fixationpauses, which serve as the beginning of the time period for averaging.
B. Characteristics of lambda waves and lambda responses:
The lambda waves and lambda responses show some characteristics similarto the visual evoked potential (VEP). For instance, neither the lambda wavenor the lambda response was observed in the dark or when the participant closedhis eyes (Scott & Bickford, 1967; Yagi, 1982). In a series of studies,we reported that the lambda response changes in relation to the physical propertiesof the stimulus object (e.g. spatial frequency, brightness,contour of the visual stimulus) (Yagi, Ishida & Katayama, 1993).Furthermore, the response varies with attention to the stimulus (Yagi,1981).As mentioned above, only a few participants have shown lambda waves thatare visible in the raw EEG. We do not yet know why some people show such large lambda waves and why some don’t. Also, the origins of the lambda waveand the lambda response are not definite. The large lambda waveobtained from the participant showed variation in amplitude.However, the cause of the variation was not clear.
Therefore, we developed a biofeedback system for lambda waves bymeans of a modified adaptive correlating filter in order to study therelationship between the lambda wave and the subjective impression(Konishi, Takeda & Yagi, 1994).
C. Biofeedback system with an adaptive correlating filter:
The adaptive correlating filter (ACF) is used to detect unknownsignals from background noises that are not time-locked to stimuli inthe ERP study (Woody, 1967). In the experiment, the template of thelambda response was previously obtained by averaging EEGs at fixationpauses in a separate trial. In the biofeedback trials, a short EEGepoch (about 200 ms) was sampled after each eye fixation. Usually, theduration of the fixation was about 240 ms. We compared each EEG epoch to the template by calculating the correlation (or thecross covariance). The feedback signal (i.e., a frequency-modulatedsound) was presented, under the control of the level of correlation.Since the sound was presented at each fixation, the participant could beaware of the appearance of each lambda wave.
D. Biofeedback of lambda waves:
1 In the first experiment, the participants who showed observablelambda waves in the routine EEG were selected. Electrodes were placedon Oz (refers to the ear lobe) for EEG and on the outer acanthi for EOG.Both the EEG and EOG were amplified and recorded into a computer.
The experiment consisted of two sessions. In the first session,the template formation session, the participant moved his eyes betweentargets on a stripe pattern, thus producing a clear lambdaresponse. EEGs were averaged at the moment of onset of fixation pausesin order to obtain the lambda response that was used as a template inthe next session.
In the second session, the biofeedback session, the participant faceda fine random pattern with low contrast. The pattern looked like aplate of gray wall, unless the participant attended to the patterndetails. EEG and EOG were processed with the ACF system. When a clearlambda wave appeared in EEG, a high pitch tone was fed back. In theincrease trial, the participant was instructed to turn on the higher tone,which would be heard after each eye movement. In the decrease trial,the task was to turn on a lower tone.
In both trials, the participant wasasked to move his or her eyes freely and to refrain from stopping for a long period of time at any oneposition.As a result, he or she became aware of the subjective conditionof the appearance of the lambda wave. Even though the lambda waves showedvariations, the participant could nevertheless consistently control the appearanceof the lambda wave during the trial. The participant concentrated attentionon the texture of the fine random pattern in order to make high tones(i.e., clear lambda waves). Meanwhile, in order to make lower tones, the participant tried to think of something that was unrelated tothe trial and attempted to defocus the stimulus pattern. Thus, we were able to confirmthat lambda waves are related to visual attention.
At present, research is still in its primary stages. The ACFsystem has several elements that require improvement if more efficientbiofeedback is to be achieved. For instance, the calculation of correlation needs improvement because the system is incomplete for detection of lambdawaves out of phase (e.g., waves with jitters). A neuro-computer system or afuzzy system is more appropriate for calculation of thecorrelation.
III. Biofeedback of FM-theta Rhythm
A. What is Fm-theta?
Fm-theta is the name of the psychologically significant EEGrhythm discovered by a Japanese researcher, Tsutomu Ishihara, in the1960s (Ishihara & Yoshii, 1972). Figure 3 shows a typical Fm-thetarhythm, marked with an underline, that was recorded during a normal student’s completion of a mentalarithmetic task. The Fm-theta has a frequencyband from 6 to 7 Hz and has a large amplitude that ranges from 30 to 100 microvolts. The name "Fm-theta" denotes the dominant area and thefrequency. The maximum amplitude is always recorded from the frontalmidline, i.e., Fz of the 10/20 system. It is interesting that Fm-thetaappears during a continuous mental task with eyes open. However, Fm-theta also appears during sleep,especially during stage-REM and stage-1 of non-REM sleep (Hayashi,Iijima, Sugita, Teshima, Matsuo, Yasoshima, Hishikawa &Ishihara, 1987).
----------- Figure 3 -----------
EEG researchers have explored many questions about Fm-theta since itsdiscovery. The main discussion usually consists of whether the rhythm itself isnormal or abnormal, and whether it is an artifact.
B. Individual Differences:
B.1. Incidence of Fm-theta subjects –
The most difficult problem with Fm-theta is that not allparticipants actually show it. Yamaguchi (1981) reviewed several reports onFm-theta during mental arithmetic task and estimated that the percentage ofparticipants who showed Fm-theta was 50%, with a range of 32 to 73%.This large difference among researches can be explained using twofactors, the sampling bias of participants and differences in the type ofmental task given. Ishihara and Yoshii (1972) compared 14 types ofmental tasks and their relative ability to evoke Fm-theta in a participant. They concluded that systematic andstep-wise thinking tends to evoke more Fm-theta. If the task is fixedto a continuous addition task, the incidence of Fm-theta subjects is about 40 to 60%. A sex difference in Fm-theta appearance during thesame task was not found in any one controlled experiment (Nishijima, Mukasa, Matsuki, Mizuki, Inanaga, Isozaki & Tanaka, 1980).
As a result of these circumstances, a Fm-theta researcher must select onlythose participants whom he knows to show this EEG pattern by means of prior tests. Whydoes Fm-theta not appear in half of the participants? What factors influence whether or not Fm-theta appears?
B.2. Normal or Abnormal –
Generally, many psychiatrists recognize theta rhythm during full wakefulness asan abnormal EEG pattern. Thus, Fm-theta that is recordedduring a mental task would be regarded as a reflection of drowsiness or a brain dysfunction such as epilepsy. In an early stage ofresearch on Fm-theta, this EEG rhythm was classified as a typicalEEG pattern observed in a population of juvenile delinquents (Yoshii,Ishihara & Tani, 1964).
However, Niwa, Yamaguchi, Hino & Tsujimoto (1975)reported that participants who showed considerable amounts of Fm-theta during the task werenot abnormal or neurotic. In their experiment, both theincidence of Fm-theta participants and the amount of Fm-theta during thetask were not different between the normal and abnormal populations definedby CMI. Additionally, Mizuki, Tanaka, Isozaki and Inanaga (1976)reported that the participants who showed a lot of Fm-theta during the taskwere recognized as extroverted and less neurotic with MPI.
B.3. Personality and psychological state –
Some personality factors,such as anxiety (measured by MAS) as well as extroversion/introversion are correlated with the appearance of Fm-theta (Niwa et al., 1975;Mizuki et al., 1976). Additionally, in a population of sportsmen, membersof a collegiate American football team tended to show more Fm-theta than members of an orchestra club in the same university(Yasumo, Kuwano, Yamaguchi, Tsujimoto, Yamada & Yamasaki, 1981).Moreover, psychological states such assleepiness, attention concentration and emotional fluctuation play an important role in the appearance of Fm-theta. Mizuki, Tanaka, andInanaga (1982) conducted a well-controlled experiment in which they found that centrally acting drugs affect theappearance of Fm-theta. They foundthat when drugs lower the state anxiety score, Fm-theta during asimple addition task increases. Mukasa (1980) found that an injectionof alcohol significantly increases the appearance of Fm-theta. Fromthese experiments, Mizuki and his collaborators undertook a systematicresearch concerning the relationship between state anxiety and Fm-theta. They concluded that the appearance of Fm-theta or an increase inthe amount of Fm-theta is a biological sign of a reduced stateanxiety.
While observing 7-12 year oldchildren, Yamada (1992) reported that Fm-theta appeared more often when subjectsfelt pleasant during the experimental task. The amount of Fm-theta during a video game task was higherthan during two other tasks. Children reported that they felt mostpleasant during the video game. Additionally, blink rate during thevideo game task was lowest. This result suggests that Fm-theta is anindex of pleasantness as well as visual attentiveness.
C. Biofeedback of Fm-theta:
C.1. Subjective experience of Fm theta –
When Fm-theta appears, what kind of associated feelings are there? In one experiment, the feedback signal was presented during or justafter the appearance of Fm-theta. The procedure of this experiment wasvery similar to the biofeedback training of Fm-theta. Participants were instructed to reportwhat happened and what they felt whenever they were presented with feedback buzzersignals. Almost all of them reported that theyconcentrated on a single task and that attention was not divided into any otherevents when they heard the buzzer.
C.2. Possibility of voluntary control of Fm-theta –
Yamaguchi and Niwa (1974) systematically evaluated thepossibility of enhancing the appearance of Fm-theta by a biofeedbacktechnique using seven participants from a previous experiment who showed high levels of FM-theta. In the experiment, five out of seven participantssucceeded in controlling the appearance of Fm-theta using auditoryfeedback during a simple addition task. The biofeedback was effective when the feedback signal was presented 3 secondsafter the initiation of Fm-thetaburst and when it lasted less than 3 seconds.
Ishihara (1981) employed a biofeedback method to study the subjective feelings of participants in his study when Fm-thetaappeared. In this experiment,twelve out of thirty participants succeeded in discriminating whether or not thegiven states related to the appearance of Fm-theta. Participants’ reports suggest that attention concentration is closely related tothe state of Fm-theta.
In another study, EEG was monitored during therest period. The feelings participants had when Fm-theta appeared were different fromemotions they felt during the appearance of alpha rhythm and alpha attenuation(Ishihara, Deumi & Iwashita, 1979).
Yamada, Morishita and Yamasaki (1987) demonstrated thepossibility of self-control of Fm-theta by auditory biofeedbacktraining by conducting a well-controlled, well-designed experiment. They found thatfeedback training for four days significantly lengthened the meanduration of Fm-theta bursts in the non-feedback control test session asshown in Figure 4. Mean Fm-theta appearance time also increased as afunction of training.
C.3. Effect of Fm-theta control –
In the experiment by Yamada et al. (1987), state anxiety scoresalso decreased after biofeedback training of Fm-theta. Althoughdecreases in state anxiety scores did not significantly differ fromintact control group and autogenic training control groups, it is wise to notethat the reduction of state anxiety related to the appearance of Fm-theta. As Ide, Akiyama, Tanaka, Takii, Mizuki & Inanaga (1982)reported, autogenic training also reduced state anxiety scores andincreased Fm-theta appearance time. In the EEG study of meditation, itis now recognized that, at the initial stage of meditation, Fm-thetais frequently observed in Yoga meditation (Yamazaki, Mitsuhashi &Yamada, 1988) and in Qi meditation (Li, Tsuda, Yamaguchi, Mizutani &Yamada, 1993).In conclusion, the result of self-control of Fm-theta might lead aperson to some states of meditation (e.g., low anxiety and high concentration).
Unique Biofeedback Researches Besides EEG in Japan
A. Biofeedback of eye movements:
After blind people have a corneal transplant operations to gain sight, they often times complain that they can not see anything in spite of the recovery of the optical system. One reason may be that they are unable to fixate and move their eyes properly. They requirecognitive learning. Wake and his group developed a biofeedback systemfor the training of eye movements (Wake & Yagi, 1978). EOG was transformed into a tone signal modulated by the EOG. A participant could know whether his or her eyes were fixed or had moved. Yagi and Tabuchi (1994) reported a biofeedback system for eye movements to improve sleep onset. The horizontal and vertical EOGs were fed to a vector module to calculatethe absolute value of eye movements. When the vector value exceeded agiven level, a pip tone was fed back to the participant. Participants were instructed that when their eyes moved, a pip tone would be presented.They were asked not to make tone pips. The number of eye movements decreased gradually. All participants went into stage-1 sleep, with an average time to sleep onset of approximately 12 minutes. Recently, we found that the participants in the feedback group went to sleep earlier than participants in the control group.Although these results were obtained in the laboratory, there is thepossibility of sleep onset improvement with the biofeedback of eyemovements, including those persons suffering sleep onset insomnia.
B. Sleep Detection:
Development of a noise-proof biofeedback system is required in order for wide-range application to occur. Yagi, Kuchinomachi & Kodama (1978) made a trial alpha wave biofeedback system for mobile participants. The system consists of three parts: a pick up, an analyzer and presentation of a biofeedback signal. Each part connects to the others by telemetry.
The first way to improve the signal-to-noise ratio is to prevent the noise from invading EEG at pick up, transmission and amplification. This can be achieved through the method of fixing electrodes on the head with fiber in order to maintain the stability of contact between skin and electrodes. Electrodes and a miniaturized amplifier and transmitter are attached to a headband, which further help filter out interfering noises.
The second way to reduce noise is in the analysis of EEG. The alpha wave component is filtered from EEG (1-200 Hz) sent through the telemeter. During vigorous movements, the noise remains even after filtering. It originates from vigorous movement and possesses components not only in the alpha band but also in other frequency bands. EEG is processed with a circuit in which alpha activity is detected as a signal only when it appears during absence of activity in the theta and beta bands. In this system, the alpha band was set from 8-13 Hz, the beta band was from 13-200 Hz and the theta band was from 1-8 Hz. When the output of the circuit was over a given level, the tone was generated. The tone was fed back to the participant through wireless means.
The system was tested under conditions of sitting, walking, running and gymnastics. It worked satisfactorily in all conditions except running. The system possesses the possibility of sleep detection in the industrial field. The development of a biofeedback system that can withstand vigorous vibration remains for the future.
Nishimura, Kosaka and Tsunemitsu (1983) reported a system of sleep detection by means of biofeedback of the skin potential level. The skin potential from the electrodes placed on the palm was amplified with a DC amplifier. The tonic component (skin potential level, SPL) was filtered and used as an index for the arousal level. According to the study, low negativity in SPL corresponds to low arousal level. The aim of the study was to suppress dozing in the participant, who was driving a practical vehicle, by alarming him or her when a low arousal level was detected. Nishimura, Kosaka and Tsunemitsu (1983) succeeded in suppressing dozing in the experiment. However, they commented that the drivers showed habituation to the alarm sound.
Asoh, Kuroki and Matsuno (1978) reported the possibility of sleep detection by using combination patterns of EEG and eye blinks. Although the studies mentioned above might be interesting from the standpoint of potential applications of biofeedback, they were inconvenient because the methods required attaching electrodes to the driver.
When the arousal level of a driver decreases, he or she cannot handle steering the car in a satisfactory manner. In their study, Asoh, Kuroki and Matsuno (1978) analyzed the handling performance of the steering wheel in order to alarm the driver of a decrease in arousal level. The method may be more effective in practical driving conditions rather than in biofeedback systems through the use of physiological responses.
Biofeedback research in Japan started in the mid-sixties. Throughout this chapter, we haveattempted to introduce and describe those studies that may be unfamiliar in foreign countries. Therefore, this chapter is not a review of all of Japan’s biofeedback research. The reader who wishes to know about other research in Japanshould refer to three issues of Current Biofeedback Research in Japan. These issues were published in English upon the Third International Conference on Biobehavioral Self-Regulation and Health in Tokyo in 1993.
In Japan, much of the recent biofeedback research has been in clinical applications rather than basic study. Many expect to use biofeedback as a tool for research in the fields of cognitivepsychology and engineering in the future.
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Fig.1. Biofeedback System for EEG and EMG.
Fig.2. Detection of Lambda Response.
Fig.3. Typical Fm-theta waves.
Fig.4. Fm-theta duration for each group.