Abstract—EEG-Based mental task classification is an

approach to understand the processes in our brain which lead

to our thoughts and behavior. Different mental tasks have been

used for this purpose and we have chosen relaxation and

imagination for our study. As well as normal conscious state,

we have considered mental tasks performed in hypnosis which

is defined as a state of consciousness with high concentration.

To assess nonlinear dynamics, we have considered fractal

dimension in addition to frequency features. HMM classifiers

have been used for classification. Results show the most

important features in EEG signal related to mentioned mental

tasks as well as differences between normal and hypnotic states

of the brain.

I. INTRODUCTION

T has been shown that people can control their brain’s

rhythms by performing a specific mental task. Therefore,

EEG-based mental task studies have been selected as an

approach to understand brain activity. EEG signal is

recorded and after preprocessing and feature extraction it is

classified to a number of predefined classes of mental tasks

which are mostly movement imagery, relaxation, complex

problem solving, mental letter composing, visual counting,

geometric figure rotation [1]. Study of mental tasks' specific

EEG pattern and provided models can help us in realizing

different activities of brain's subsystems and their

interaction.

Hypnosis is an altered state of consciousness which is

with high concentrated attention [2]. Based on clinical

observations, different theories have been suggested about

hypnosis. In addition, hypnosis has been studied using EEG

signal processing in works such as classification of mental

tasks as a hypnotic one or a normal one [3], or assessing the

depth of hypnosis and hypnotizability of subjects [4].

High concentration gained through hypnosis can remove

problems such as distraction from performing the asked

mental task. To find out how hypnosis can help us in mental

task studies, we have compared mental task classification in

hypnosis and normal states. Classification of two mental

Manuscript received May 2, 2005; revised Jul 15, 2005. This work was

supported in part by the Neuropsychiatry Research, Research Center

for Intelligent Signal Processing, Tehran, Iran.

S. Solhjoo is with the Biomedical Engineering Faculty, Amirkabir

University of Technology, Tehran, Iran (corresponding author, phone: +98-

9173134754; fax: +98-21-8063547; e-mail: soroosh@bme.aut.ac.ir).

A. Motie Nasrabadi is with the Biomedical Engineering Department,

Engineering Faculty, Shahed University, Tehran, Iran (e-mail:

amotie_n@aut.ac.ir).

M.R. Hashemi Golpayegani is with the Biomedical Engineering Faculty,

Amirkabir University of Technology, Tehran, Iran (e-mail:

hashemi_g@morva.net).

tasks has been studied: 1. Relaxation, 2. Imagination.

Frequency and chaotic features have been extracted from

EEG signal, while modeling and classifying of raw signal

have been studied as well. HMM (Hidden Markov Model)

-based classifiers have been used for classification purpose.

II. DATA COLLECTION AND PROCESSING

A. Subjects and Mental Tasks

We have used EEG signals recorded during hypnotism

sessions in RCISP by Abootalebi et al. [3]. EEG signals we

have used were of 5 men with hypnotizability scores of 4 or

5 according to SHCS1 standard test, i.e. high hypnotizability.

All subjects were right-handed and right-eared (with right

hand and ear dominant). Both tasks of relaxation and

imagination were performed by the subjects in both normal

and hypnotic states. The subject’s eyes were closed, and he

was relaxed on a comfortable chair during the recordings.

First step was relaxation for 2 minutes. Then in imagination

task, the subject was asked to imagine himself in front of a

blackboard in a classroom. Then he should draw a circle on

the blackboard and write the number “1” in it. Then it was

erasing that number and writing the next number repeatedly,

in an increasing manner, for two minutes. After these two

steps, hypnotism process was started according to SHCS.

Relaxation and the same imagination were performed in

hypnosis as well. Each hypnotic task was performed by

subjects in a period of 1 or 2 minutes [3].

TABLE I: AGE AND SHCS HYPNOTIZABILITY SCORES OF SUBJECTS [3]

Subject HH1 NDR1 RNP1 ASZ1 MRJ1

Age 36 32 23 30 32

Hypnotizability

Score5 4 5 4 4

B. Data Acquisition and Preprocessing

Electrodes were placed according to 10-20 standard. EEG

signal was sampled at 256 Hz and filtered using an elliptic

filter with band-width of 0.5-30 Hz and then down-sampled

to 128 Hz.

For each subject, a 1-minute segment of each task was

selected to be processed. Each 1-minute segment was

divided into 12 5-second segments. Segments with eye

blinking and movement artifacts were deleted manually.

1 Stanford Hypnotic Clinical Scale

EEG-Based Mental Task Classification in Hypnotized and Normal

Subjects

Soroosh Solhjoo, Ali Motie Nasrabadi, Mohammad Reza Hashemi Golpayegani

I

Proceedings of the 2005 IEEEEngineering in Medicine and Biology 27th Annual ConferenceShanghai, China, September 1-4, 2005

0-7803-8740-6/05/$20.00 ©2005 IEEE. 2041

Against the movement imagery mental tasks, that the

involving brain segments are almost specified, in the tasks

mentioned here, there is no especial segment of brain

specified to be responsible. Therefore, while data just from 2

or 3 EEG channels are enough to be considered in motor

imagery classification [5], data from all channels were used

in this study. We have applied a surface Laplacian (SL)

filter to reveal the actual signal produced only by local

sources below each electrode [6]. Doing this we decrease

data to signals only from 6 SL channels.

C. Extracted Features

Using raw EEG signal and trying to classify the

mentioned mental tasks with HMM classifiers led to low

percentage of correct classification for most of subjects in

hypnosis state, while results in normal state were

satisfactory. For only one of the subjects (MRJ1), we

reached good result (100% classification accuracy) in

hypnosis. So we turn to using frequency and chaotic features

for our classification purpose.

Parameters that represent chaotic behavior may be divided

to two categories. The First category indicates dynamic

behavior. Maximum Lyapunov exponent (MLE) is of this

category. These parameters state how the system behaves on

the nearby trajectories. The second category emphasizes the

geometric property of basin of attraction. Fractal dimension

(FD) is of this category. These dimension show geometrical

property of attractors and is also computed very fast [4].

Our goal was to associate each 5-second segment data as

a trial to its corresponding class. To do this, features were

extracted form each 1-second segment with 50% overlap,

and sequence of 9 extracted features was considered as the

feature vector of a 5-second segment, which was to be

modeled and classified.

Features were as follows:

1. Power spectral density components;

2. Petrosian Fractal Dimension;

3. Higuchi Fractal Dimension.

Power spectral density components of each SL channel

for frequencies 2 to 30 Hz with the frequency resolution of 2

Hz (15 feature for each of 6 SL channel) are extracted.

Segments of 0.5 second were averaged. A Hamming

window was applied to each segment, and the overlapping

between the segments was 50% [5].

Petrosian’s algorithm uses a quick estimate of the FD:

)*4.0

(loglog

log

1010

10

Nn

nn

nFD

where n is the length of the sequence, and N is the

number of sign changes in derivative of the signal [4].

In Higuchi’s algorithm, k new time series are constructed

from the signal x(1), x(2),…, x(N) under study:

)(),...,2(),(),( kk

mNmxkmxkmxmxx k

m

where m = 1, 2, …, k and k indicate the initial time value,

and the discrete time interval between points, respectively.

For each of the k time seriesk

mx , the length Lm(k) is

computed by:

kk

mN

Nkimxikmx

kL im

)1())1(()(

)( 1

where N is the total length of the signal x. An average

length is computed as the mean of the k lengths )(kLm (for

m = 1, 2, …, k). This procedure is repeated for each k

ranging from 1 to kmax, obtaining an average length for

each k. In the curve of ))(ln( kL versus )/1ln( k , the slope of

the best fitted line to this curve is the estimate of the fractal

dimension [4].

III. DATA ANALYSIS AND RESULTS

A. Classifiers

Classifying 1-second segments of data using different

features and classifiers led to low classification accuracy. So

we considered the temporal trend of EEG signal and

segments of 5-second length were to be classified.

We have used HMM classifiers. HMM models with

different number of states (ranging from 1 to 8) and different

number of Gaussian mixtures for each state (ranging from 1

to 8) were used to model feature vector sequences.

Classification was done according to log-likelihood

measure. Results are the best classification accuracy

percentage of different model structures.

According to low number of data segments (12 5-second

segments for each task/subject), we used LOO (Leave One

Out) cross validation method [7]. In this method, the

training and test sessions repeat to the number of data

segments available, and then classification accuracy

percentage is specified. In each session, one segment is left

out as the test data, and other segments are considered as the

training data.

For some of train and test sessions we did LOO method

for 5 times to test our classifiers for effect of initial

conditions. Low and in most of cases no variance of results

showed that their performance is not dependent on this

factor.

For each trial (5-second segment) there is a sequence of 9

feature vectors. Using HMM classifiers, this data is used as

a sequence of data with length 9 and dimension 90 (in the

case of the frequency features).

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B. Results

At first we considered HMM classifiers applied to

frequency features. Results are in a good range comparing to

our previous studies on raw signal data, but dimension of

feature vectors (90) is large which leads to large and

complex HMM models that need long time to be trained.

TABLE II: MENTAL TASK CLASSIFICATION ACCURACY ACCORDING TO

FREQUENCY FEATURES USING HMM CLASSIFIER FOR 3 SUBJECTS

Subjects Hypnosis Normal

HH1 83.33 % 87.50 %

NDR1 68.18 % 100 %

RNP1 87.50 % 100 %

Decreasing the length of feature vector did not lead to

better results, so we turned to test other features.

To consider nonlinear dynamics of EEG signal, we

selected fractal dimension. Fractal dimension was calculated

for each 1-second segment for each of 6 SL channels,

providing a feature vector of dimension 6. Sequence of 9

feature vector for a 5-second interval was to be modeled and

classified.

At first, we considered fractal dimension calculated based

on Petrosian algorithm. In this case, we did not get good

results as they can found in TABLE III. In some cases

results are worse than using of raw data.

TABLE III: MENTAL TASK CLASSIFICATION ACCURACY ACCORDING TO

PETROSIAN FRACTAL DIMENSION USING HMM CLASSIFIER FOR 3 SUBJECTS

Subjects Hypnosis Normal

HH1 68.18 % 75.83 %

NDR1 77.27 % 58.33 %

MRJ1 95.00 % 53.64 %

Considering Higuchi fractal dimension instead, showed

very good results as it can be seen in TABLE IV.

TABLE IV: MENTAL TASK CLASSIFICATION ACCURACY ACCORDING TO

HIGUCHI FRACTAL DIMENSION USING HMM CLASSIFIER FOR 4 SUBJECTS

Subjects Hypnosis Normal

HH1 86.36 % 95.83 %

NDR1 72.73 % 79.17 %

RNP1 91.67 % 100 %

ASZ1 68.18 % 95.83 %

Considering low correct classification percentage for

classifying of hypnotic mental tasks of subject ASZ1, we

used asymmetry signal of Laplacian channels and then

Higuchi fractal dimension was extracted from that signal.

Asymmetry signal was produced as (R-L)/(R+L), where R

and L are EEG signal from right and left Laplacian

channels, respectively [1, 8]. Sequence of 9 Higuchi fractal

dimensions of these three new signals are applied to

classifiers. Results of using such a feature are shown in

TABLE V, for classification of two data sets of hypnotic

mental tasks done by HH1 and ASZ1.

As it can be seen there is a very high increase in

performance of HMM classifier for ASZ1 (20%), while

there is a very low decrease for HH1 (5%).TABLE V: COMPARISON OF MAXIMUM CLASSIFICATION ACCURACY

PERCENTAGE, DECREASING THE NUMBER OF FEATURES USING

ASYMMETRY DATA

Subjects Higuchi Higuchi_Asymmetry

HH1_Hypnosis 86.36 % 81.82 %

ASZ1_Hypnosis 68.18 % 87.50 %

IV. DISCUSSION AND CONCLUSION

We have applied HMM classifiers to some different

frequency and chaotic feature vectors, to classify mental

tasks performed in normal and hypnotic states. HMM

classifiers with different number of states and Gaussian

mixtures were studied.

It has been shown that considering temporal trend of data

and long term study of EEG signals leads to better results.

To reach good results, we turn to use fractal dimension

features.

While Petrosian fractal dimension led to worse results

compared to raw data studies, using Higuchi fractal

dimension led to very good results, as we saw about 20%

increase in accuracy percentage in classification of hypnotic

and normal tasks of HH1 using Higuchi instead of Petrosian

fractal dimension. As well such an increase can be seen in

NDR1 normal mental task classification, while there is only

4.5% decrease in hypnotic mental task classification of that

subject.

The best results were of Higuchi fractal dimension

extracted from 3 asymmetry channels’ signals which were

produced from 6 Laplacian channels’ signals.

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