A NOVEL APPROACH TO ALPHA ACTIVITY TRAINING

USING WATER BASED ELECTRODES

M. K. J. Dekker

1,3

, M. M. Sitskoorn

, A. Denissen

, M. Jager

, D. Vernon

, V. Mihajlovic

and G. J. M. van Boxtel

1,2

Department of Neuropsychology, Tilburg University, Tilburg, The Netherlands

Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

Brain, Body, and Behaviour Group, Philips Research Eindhoven, Eindhoven, The Netherlands

Canterbury Christ Church University, Canterbury, Kent, U.K.

Keywords: Alpha Activity, Instrumental Conditioning, Neurofeedback, Random Beta Training, Relaxation, Water-

based Electrodes.

Abstract: Fifty healthy participants took part in a double-blind placebo-controlled study in which they were either

given auditory alpha brain activity (8–12 Hz) training (N=18), random beta training (N=12), or no training

at all (N=20). A novel wireless electrode system was used for training without instructions, involving water-

based electrodes mounted in an audio headset. Training was applied approximately at central positions C3

and C4. Post-training measurement using a conventional full-cap EEG system revealed an increase in alpha

activity at posterior sites compared to pre-training levels. This significant increase was present only in the

group that received alpha training, and remained evident at a 3 month follow-up session. In an exit

interview, approximately twice as many participants in the alpha training group mentioned that the training

was relaxing, compared to those in the control groups. Overall, results suggest that self-guided alpha

activity training using this novel system is feasible and represents a step forward in the ease of instrumental

conditioning of brain rhythms.

1 INTRODUCTION

Stress and relaxation

Keeping up with the pace of modern society seems

to be increasingly difficult for many people. It has

been estimated that work-related stress negatively

affects at least 40 million workers in 15 countries of

the European Union (European Commission,

Employment & Social Affairs, 1999, cited in Mental

health policies and programmes in the workplace,

WHO, 2005). Strategies for coping with such stress

can be diverse, like getting a massage, practicing

yoga, doing mindfulness training or even going into

cognitive therapy. Here we initiated a program

which explored whether instrumental conditioning

of alpha brain activity (by neurofeedback) could

help people to relax in an easy and enjoyable way at

low cost.

Alpha brain activity and relaxation

The human brain rhythm in the alpha range (8-12

Hz) has been associated with reduced information

processing by a decrease in cortical activity (Adrian

& Matthews, 1934; Pfurtscheller, 1991) and with a

relaxed state of mind (Lindsley, 1960). Previous

attempts for increasing alpha activity by

instrumental conditioning have been reported to

increase subjective feelings of relaxation and well-

being (Hardt & Kamiya, 1978; Nowlis & Kamiya,

1970; Watson, Herder & Passini, 1978; Rice,

Blanchard & Purcell, 1993). Unfortunately, much of

the research focusing on manipulating alpha activity

has suffered from methodological difficulties such

as the absence of adequate control conditions

(Vernon et al., 2009).

The present approach

We wanted to investigate whether instrumental

conditioning of alpha activity could be applied in a

consumer product aimed at healthy everyday users.

Because we wanted to create a pleasant form of

training, auditory feedback was given by the

person’s own favourite music. Simple high-pass

filtering on the music greatly affected music quality,

481

K. J. Dekker M., M. Sitskoorn M., Denissen A., Jager M., Vernon D., Mihajlovic V. and J. M. van Boxtel G..

A NOVEL APPROACH TO ALPHA ACTIVITY TRAINING USING WATER BASED ELECTRODES.

DOI: 10.5220/0003888904810486

In Proceedings of the International Conference on Health Informatics (BSSS-2012), pages 481-486

ISBN: 978-989-8425-88-1

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

which turned out to be the basis of a very intuitive

form of feedback because subjects know their

favourite music very well. Because of this, no

instructions were needed for the participant for

training to occur (self-guided training). In this way

relaxation training is easy: we pictured a person

sitting in the train, wearing a device similar to an

MP3 player, listening to his/her own favourite

music. Because listening to music might be relaxing

by itself, one group in our design served as control

and only listened to their favourite music. And

because experiencing changes in music quality alone

can already indicate that one is part of an

experimental group, a second control group, which

received random beta (random 4 Hz bins between

14-30 Hz) feedback, was added. Next to this strong

placebo-controlled experimental design, the

investigation was double-blind. Furthermore, if our

device is going to be used in real-life situations, the

measurement of alpha activity should be greatly

simplified: conductive gel or pastes are out of the

question. For this we used (tap) water-based

electrodes that were, as we can show, as reliable as

the conventional way. These electrodes, mounted in

the auditory headset, were used as an intermediate

step towards dry electrodes.

2 METHOD

2.1 Study Design

Sixty-two healthy students were randomly assigned

to our three groups: alpha training (A), random beta

training (B), or music only group (C). Unfortunately,

twelve persons had to be excluded from our final

analysis either because they failed to complete the

training sessions or because of technical problems

during training sessions. Eventually, 50 participants

(mean age 21) were in one of the three groups: A: 6

male, 12 female; B: 3 male, 9 female; C: 5 male, 15

female. Before and after 15 training sessions on

consecutive days, a quantitative electro-

encephalograph (QEEG) using a Neuroscan system

with 26 electrodes (according 10-10 system) was

taken, together with several questionnaires. After

three months, a follow up measurement took place to

investigate possible long-term effects.

2.2 Training Sessions

One training session (of about 1 hour) contained two

baseline measurements of 5 minutes rest with eyes

open and 5 minutes rest with eyes closed. After that,

3 neurofeedback training intervals of 8 minutes were

interspersed by cognitive tasks of 5 minutes each

(Flanker, Stop-signal, Stroop, N-back). The purpose

of this sequence was fourfold: we wanted to avoid

participants falling asleep during one long relaxation

session; we wanted to assess the effects of alpha

training on simple cognitive tasks administered

longitudinally; by alternating relaxation and

cognitive work we hoped to capture details relating

to the dynamics of the alpha rhythm; and it allowed

us to match alpha levels to the audio filter. Every

participant was asked to keep their eyes open during

the neurofeedback intervals. Alpha training might be

less effective during eyes closed, where alpha power

is generally higher and may cause a ceiling effect in

training.

2.2.1 Alpha Activity Training Tool

During training sessions each participant was seated

in a comfortable chair in front of a laptop. The

participants were given a set of headphones that they

used for listening to their favourite music (all types

of music were allowed). EEG was measured using

Ag/AgCl ring electrodes, roughly positioned over

C3 and C4, mounted at the inner side of the headset

band. Electrodes A1 and A2, mounted in the ear

covers, served as reference (C3-A1, C4-A2).

Horizontal and vertical EOG was measured from

electrodes at the outer canthi and above and below

the left eye, respectively. The quality of the data was

assessed on-line via the ratio of the power in the 49-

51 Hz (noise) range, relative to the power in the 4-35

Hz (signal) range. The signals were amplified (DC-

400 Hz) and sampled at a rate of 1024 Hz by a 24 bit

A/D converter on a Nexus-10 portable device

(MindMedia B.V., The Netherlands). The signals

were transmitted via Bluetooth to a PC that

controlled the experiment, and stored the data.

Electromagnetic influence (EMI) was eliminated in

our training via the 100 dB common-mode rejection

ratio (CMRR) of our Nexus-10 device.

2.2.2 Neurofeedback Protocol

Eight times per second (each 125 ms) a segment of

the preceding 4 seconds was filtered by a third-order

Butterworth band-pass filter from 4-35 Hz, and a

first order notch filter at 50 Hz. To be usable for a

feedback update, this segment should fulfil three

criteria: (i) no clipping or overflow of the amplifier;

(ii) peak-to-peak of at most 200 μV; (iii) the ratio

between the line noise (49-51 Hz) and EEG power

(4-35 Hz) should be smaller than 1.0. For each

channel (C3 and C4), the relative alpha power was

calculated as the sum of the power in the alpha band

HEALTHINF 2012 - International Conference on Health Informatics

482

(8-12 Hz), divided by the sum of total power (4-35

Hz). A low cut-off frequency of 4 Hz was used to be

relatively free of slow drift artefacts. If both

channels showed a good epoch (fulfilling the 3

criteria) of 4 seconds, then the average of the two

channels were used. If neither channels resulted in a

good epoch, then no feedback update was given. The

relative alpha measure was filtered by a first-order

IIR filter with a time constant of 4 seconds to

smoothen the speed of the changes in feedback

somewhat. The relative alpha measure was used to

drive the cut-off frequency of a first-order high-pass

filter built into the audio path of the music played

through the headphones. This cut-off frequency

equalled 2 Hz if the current alpha level was larger

than the maximum alpha level for the previous part,

and 2000 Hz if the current alpha level was lower

than the minimum alpha level for the previous part.

For intermediate levels, a linear interpolation was

done using the following formula: fc=2000–(2000-2)

* (alpha-alpha_min)/(alpha_max-alpha_min), where

fc= audio cutoff frequency, alpha =current alpha

level, min_alpha= minimum alpha level,

max_alpha= maximum alpha level. This procedure

resulted in a very intuitive feedback mechanism in

which a person’s own well-known favourite music

sounded thin and distant if alpha levels were low

because the low tones were removed. When alpha

levels were high the music sounded rich and full. In

the random beta group, similar neurofeedback was

given. Only, the EEG frequency used to drive the

audio filter was always between 14 and 30 Hz. Beta

bands (4 Hz) on different days did not overlap. The

minimum and maximum beta levels were calculated

from the first baseline (eyes open) measurement of

that day in the band that was chosen for that day. In

the music only group, no feedback was given. The

participants always listened to their favourite music

in full quality.

Importantly, all participants received no

instructions about the training. They were not told

that they were expected to increase the quality of the

music; they were only asked to 'sit back and relax'.

2.3 Pre and Post Measurement QEEG

Alpha power spectral density was estimated using

the Welch (1967) method on digitally filtered EEG.

The EEG and EOG data channels were first off-line

filtered with a 50 Hz first order (6 dB down per

octave) notch filter for removing line noise, and then

by a third-order (18 dB down per octave)

Butterworth high-pass filter at 1 Hz and low-pass at

65 Hz. EEG spectra were computed and the EEG

record was then segmented in 75% overlapping

intervals of 4 seconds. Thus, each epoch was shifted

one second forward in time, and there were 120

segments in total. Horizontal and vertical EOG

channels were used to correct the EEG signal for eye

movement artefacts using a linear regression method

with separate coefficients for horizontal and vertical

eye movements (cf. Verleger, Gasser, & Möcks,

1982). This correction was only applied if the EOG

in a particular segment exceeded 60 μV. The

segments were then transformed to the frequency

domain using a Hanning window for tapering. The

FFT power values were then transformed to a log10

scale and all frequency components (0.25 Hz

resolution) were averaged to form the frequency

bands Alpha (8-12 Hz) and Total Power (4-35 Hz).

Relative alpha power was calculated as the ratio

between alpha and total power.

2.4 Statistical Analysis

We first checked all measures for normality of the

sampling distribution, and log-transformed the data

if necessary. All calculations were done using

(autoregressive) linear mixed models (SAS PROC

MIXED 9.1), with a 0.05 level of significance. We

used between-subjects factor group and within-

subjects factor session (pre, post, follow-up). Using

young healthy participants, instead of persons with a

higher stress level, could mean that the chances of

finding strong effects are not very high. On the other

hand, we reasoned that such a situation constitutes a

powerful test, and that finding such effects would be

quite meaningful and important.

3 RESULTS

3.1 Comparison of our Water-based

Electrodes with Conventional

QEEG

Figure 1 shows grand average (N= 50) power

spectra recorded from positions C3 and C4 with the

conventional full-cap QEEG on pre-measurement

and the novel system on the first training day. It

shows that the systems were remarkably alike in the

alpha frequency range; the difference at higher

frequencies arises because of the different ways in

50 Hz line noise filtering. Alpha power (8-12 Hz)

recorded with the QEEG system and with the novel

system could not be distinguished statistically

(QEEG versus headset: F(1,43) = 1.67, n.s.; Eyes

open versus eyes closed: F(1,43) = 9.87, p <0.01; C3

versus C4: F(1,43) = 0.28, n.s.). In addition,

A NOVEL APPROACH TO ALPHA ACTIVITY TRAINING USING WATER BASED ELECTRODES

483

Figure 1: power spectra from our novel system and

conventional QEEG measurements (adapted from Van

Boxtel et al., 2011).

correlations between the alpha power recorded with

the two systems were: 0.88 (C3, eyes open), 0.80

(C3, eyes closed), 0.88 (C4, eyes open) and 0.84

(C4, eyes closed) In sum, our novel system using

water-based electrodes trained alpha activity at

central sites very well.

3.2 Alpha Power in QEEG

Measurements

We tested the relative alpha power (8-12 Hz / 4-35

Hz) for posterior electrodes on C3, C4, P3, P4, O1,

and O1. As expected, alpha power was larger for

eyes closed than for eyes open (F(1,47) = 491.15, p

< 0.001, power = 1.00) conditions, and larger at

occipital and parietal electrodes compared to central

electrodes (F(2,94) = 8.06, p < 0.001, power = 0.95).

In all three groups, alpha power increased over

sessions (F(2,90) = 66.96, p < 0.001, power = 1.00).

Importantly, the increase in relative alpha power was

largest for the alpha training group (F(4,90) = 20.60,

p < 0.001, power = 1.00), especially in the eyes open

condition (F(4,89) = 6.39, p < 0.001, power = 0.99).

Figure 2 provides a visual representation of this

effect. The figure not only shows the greatest alpha

power increase in the alpha training group, but also

that alpha levels, especially in the eyes open

condition, continued to increase to follow-up

measurement three months later. Apparently, the

participants in the alpha group learned how to

control their alpha level and were able to increase it

further.

3.3 Relaxation

(For the outcome of the questionnaires, see Van

Boxtel et al., 2011). As a qualitative relaxation

Figure 2: alpha power changes over sessions in the three

groups (left: eyes closed, right: eyes open condition;

adapted from Van Boxtel et al., 2011).

measure, we examined how many participants in

each group indicated that the training sessions were

relaxing, without them being prompted. We did not

inform the participants that this study was about

relaxation, and asked them neutrally about how they

experienced the training. In the alpha training group

(A), 53% of the participants spontaneously remarked

that they experienced the training sessions as

relaxing, as opposed to 20% in the random beta

group (B) and 21% in the music only control group

(C).

4 DISCUSSION

The purpose of this study was to investigate whether

alpha activity training using an innovative self-

guided system with water-based electrodes mounted

in an audio headset was feasible.

There are several indications that supported the

feasibility of our relaxation tool. First, the system

with water-based electrodes was able to record EEG

in the alpha range, of which the spectral properties

correspond to what is usually reported in the

literature. Alpha power recorded with the

conventional QEEG system and with our novel

system was statistically indistinguishable. This fact

supports the use of our neurofeedback tool, which is

very easy and less time consuming to use: no pastes

or gels are required for training.

Second, alpha training using our novel system

resulted in an increase in alpha activity as recorded

using the conventional QEEG system. Even though

the alpha group significantly increased most in

alpha power, a weak point could be the fact that

alpha activity already differed between groups

during pre-measurement. Possibly, the music only

group did not increase in alpha power, because their

HEALTHINF 2012 - International Conference on Health Informatics

484

alpha activity already was higher at the beginning of

the experiment.

It was also found that training of alpha activity at

central sites increased alpha activity more

posteriorly. This is a quite surprising finding,

although to some extent consistent with others who

have found that neurofeedback training can lead to

changes beyond target frequency and location (e.g.,

Egner et al., 2004). In all statistical tests, alpha

activity levels were largest at the occipital

electrodes, with parietal electrodes also displaying

considerable activity. This is what is usually

reported for the classical alpha rhythm (Shaw,

2003). It is unclear how the activity in central and

posterior areas is related.

Another important result of the present study is

that the increase in alpha activity persisted, and

increased slightly, at the follow-up session three

months after the last training session. It is possible

that the participants in the alpha training group

learned to produce a solid alpha rhythm that they

could possibly evoke at will whenever needed. Such

a possibility not only supports the conclusion that

use of such an innovative self-guided system can

elicit learning, but that due to the self-guided nature

of the learning it is also more easily maintained.

However, further research is needed in order to

substantiate such a claim.

Alpha activity that can be measured over the

central scalp is called the mu or sensorimotor

rhythm (SMR). Even though this rhythm can block

in response to motor action (Pfurtscheller &

Aranibar, 1979), we wanted to know whether

training at central sites is related to subjectively

experienced relaxation and whether central training

generalises to alpha activity in other brain areas.

Namely, there is some evidence that training of brain

rhythms may generalise over the scalp (e.g., Egner,

Zech, & Gruzelier, 2004).

The inclusion of two control conditions is a

strong point of the present study, which

demonstrates trainability of alpha activity beyond

doubt. Alpha activity recorded with the eyes open

exhibited the largest increase. It is tempting to relate

these findings to the fact that the alpha training was

given with the eyes open. However, the differential

effects of training with eyes open and eyes closed on

post-training recordings of eyes open and eyes

closed activity, is not well studied (see Vernon et al.,

2009).

Using a random beta training protocol as a

control, as utilised by Hoedlmoser and colleagues

(2008), and also recently applied in the gamma

neurofeedback studies of Keizer and co-workers

(2010), seemed to work quite well. Importantly, the

random beta group did not get any adverse effects of

the random beta training. Hence, random beta might

be considered an appropriate control. For the alpha

training group, a training approach based on the

individual alpha peak frequency (IAF; Klimesch,

1996) would allow, in the future, for a more

individualised and probably more effective training

scheme.

We did see that alpha activity training produced

more alpha activity along with subjective relaxation

than the control groups did. This is surprising

because listening to music by itself could be

considered to be a relaxing experience. Our research

would suggest that combining alpha activity training

with listening to music adds an extra degree of

relaxation.

However, using the participant’s own favourite

music for auditory feedback might have disturbed

our relaxation goal, because of the fact that music

might arouse one (Van der Zwaag et al., 2011). We

tried to filter this influence out as much as possible

by using the participant’s own favourite music they

liked to hear during training sessions. Again, any

sort of music was allowed. Maybe it would be nice

to combine studies (Van der Zwaag et al., 2011) to

create even more efficient alpha training and

optimize relaxation effects.

To sum up, we compared training of alpha

activity to training of random beta and music only

control, and found that alpha activity training at

central sites enhanced alpha activity at posterior

sites. We have demonstrated the feasibility of alpha

activity training using an easy, wireless, water-based

electrode system combined with an intuitive form of

auditory feedback based on the participant’s own

favourite music. We consider our system to be an

important step forward in the development of a

system for instrumental conditioning of brain

rhythms that can be used both in the clinic as well as

at home. In our view, the availability of such a

system would solve a number of technical

weaknesses that surround such systems currently, so

that more emphasis can be placed on factors that

really matter in these studies, such as the protocols

to be used, the number and nature of the training

sessions, the influence of different instrumental

conditioning schedules, and the transfer of the

trained brain area to other areas, to name but a few.

REFERENCES

Angelakis, E., Stathopoulou, S., Frymiare, J. L., Green, D.

A NOVEL APPROACH TO ALPHA ACTIVITY TRAINING USING WATER BASED ELECTRODES

485

L., Lubar, J. F., & Kounios, J. (2007). EEG

neurofeedback: a brief overview and an example of

peak alpha frequency training for cognitive

enhancement in the elderly. The Clinical

Neuropsychologist, 21(1), 110-129.

Berger, H. (1929). Über das Elektrenkephalogram des

Menschen. Archiv für Psychiatrie, 87, 527-570.

Bazanova, O., & Aftanas, L. (2008). Individual EEG

alpha-indices and nonverbal creativity in female

during the spontaneous menstrual cycle in comparison

with male subjects. Annals of General Psychiatry, 7,

S101.

Cabral, R.J., & Scott, D.F. (1976). Effects of two

desensitization techniques, biofeedback and relaxation,

on intractable epilepsy: follow-up study. Journal of

Neurology, Neurosurgery, and Psychiatry, 39, 504-

507.

Chorbajian, T. (1971). Nomographic approach for the

estimation of heart rate recovery time from exercise in

humans. Journal of Applied Physiology, 31, 962-964.

Dempster, T., & Vernon, D. (2009). Identifying indices of

learning for alpha neurofeedback training. Applied

Psychophysiology and Neurofeedback, 34, 309-318.

Egner, T., Strawson, E., & Gruzelier, J.H. (2002). EEG

signature and phenomenology and alpha/theta

neurofeedback following training versus mock

feedback. Applied Psychophysiology and

Biofeedback, 27, 261-270.

Egner, T., Zech, T.F., & Gruzelier, J.H. (2004). The

effects of neurofeedback training on the spectral

topography of the electroencephalogram. Clinical

Neurophysiology, 115, 2452-2460.

Goldman, R.I., Stern, J.M., Engel Jr., J., & Cohen, M.S.

(2002). Simultaneous EEG and fMRI of the alpha

rhythm. Neuroreport, 13, 2487-2492.

Gruzelier, J.H. (2002). A review of the impact of

hypnosis, relaxation, guided imagery and individual

differences on aspects of immunity and health. Stress,

5, 147-163.

Hardt, J. V., & Kamiya, J. (1978). Anxiety change through

electroencephalographic alpha feedback seen only in

high anxiety subjects. Science, 201(4350), 79-81.

Herbert, R., & Tan, G. (2004). Quantitative EEG phase

evaluation of Transcendental Meditation. Journal of

Neurotherapy, 8(2), 120-121.

Keizer, A.W., Verschoor, M., Verment, R.S., & Hommel,

B. (2010). The effect of gamma enhancing

neurofeedback on the control of feature bindings and

intelligence measures. International Journal of

Psychophysiology, 75, 25-32.

Klimesch, W. (1997). EEG alpha rhythms and memory

processes. International Journal of Psychophysiology,

26, 319-340.

Klimesch, W. (1999). EEG alpha and theta oscillations

reflect cognitive and memory performance: a review

and analysis. Brain Research Reviews, 29, 169-195.

Klimesch, W., Sauseng, P., & Hanslmayr, S. (2007). EEG

alpha oscillations: The inhibitiontiming hypothesis.

Brain Research Reviews, 53, 63-88.

Lindsley,

D.B. (1960). Attention, Consciousness, Sleep

and Wakefulnes. Handbook of Physiology:

Neurophysiology. American Physiological Society.

McNair, D. M., Lorr, M., & Droppleman, L. F. (1971).

Manual for the Profile of Mood States. San Diego,

CA: Educational and Industrial Testing Services.

Mental Health Policies and Programmes in the

Workplace (2005). Geneva, World Health

Organization (Mental Health Policy and Service

Guidance Package).

Nowlis, D.P., & Kamiya, J. (1970). The control of

electroencephalographic alpha rhythms through

auditory feedback and the associated mental activity.

Psychophysiology, 6, 476-484.

Nunez, P.L. (1995). Neocortical Dynamics and Human

EEG rhythms. London: Oxford University Press.

Perlini, A.H., & Spanos, N.P. (1991). EEG alpha

methodologies and hypnotisability: A critical review.

Psychophysiology, 28, 511-530.

Pfurtscheller, G., & Aranibar, A. (1979). Evaluation of

event-related desynchronization (ERD) preceding and

following voluntary self-paced movement.

Electroencephalography and Clinical

Neurophysiology, 46, 138-146.

Rice, K. M., Blanchard, E. B., & Purcell, M. (1993).

Biofeedback treatments of generalised anxiety

disorder: Preliminary results. Biofeedback and Self

Regulation, 18, 93-105.

Shaw, J.C. (2003). The Brain’s Alpha Rhythms and the

Mind. Amsterdam: Elsevier.

Vander Sloten, P. Verdonck, M. Nyssen & J. Haueisen

(Eds.). IFMBE Proceedings, 22, 1022-1025.

Thatcher, R.W., Krause, P.J., & Hrybyk, M. (1976).

Cortico-cortical associations and EEG coherence: a

two-compartment model. Electroencephalograhy and

Clincical Neurophysiology, 64, 123-143.

Verleger, Gasser, T., & Möcks, J. (1982). Correction of

EOG artifacts in event-related potentials of the EEG:

Aspects of reliability and validity. Psychophysiology,

19, 472- 480.

Van Boxtel, G.J.M., Denissen, A., Jager, M., Vernon, D.,

Dekker, M.K.J., Mihaljovic, V. & Sitskoorn, M.M.

(2011). A novel approach to alpha activity training.

International journal of psychophysiology, In Press.

Vernon, D. (2005). Can neurofeedback training enhance

performance? An evaluation of the evidence with

implications for future research. Applied

Psychophysiology and Biofeedback, 30, 347-364.

Vernon, D., Dempster, T., Bazanova, O., Rutterford, N.,

Pasqualini, M., & Andersen, S. (2009). Alpha

neurofeedback training for performance enhancement:

Reviewing the methodology. Journal of Neurotherapy,

13, 1-13.

Watson, C. G., Herder, J., & Passini, F. T. (1978). Alpha

biofeedback theory in alcoholics: An 18-month

follow-up. Journal of Clinical Psychology, 34(2), 765-

769.

Welch, P.D. (1967). The use of fast Fourier transforms for

the estimation of power spectra: A method based on

time averaging over short modified periodograms.

IEEE Transactions on Audio and Electroacoustics, 15,

70-73.

HEALTHINF 2012 - International Conference on Health Informatics

486