eeg interface module for cognitive assessment through neurophysiologic tests

8/10/2019 Eeg Interface Module for Cognitive Assessment Through Neurophysiologic Tests

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(IJCRSEE) International Journal of Cognitive Research in Science, Engineering and Education

Vol. 2, No.2, 2014.

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Abstract. The cognitive signal processing is one

of the important interdisciplinary eld came from areas

of life sciences, psychology, psychiatry, engi-neering,

mathematics, physics, statistics and many other felds

of research. Neurophysiologic tests are utilized to assess

and treat brain injury, dementia, neurological conditions,

and useful to investigate psychological and psychiatric

disorders. This paper presents an ongoing research work

on development of EEG interface device based on the

principles of cognitive assessments and instrumentation.

The method proposed engineering and science of cogni-

tive signal processing in case of brain computer in-terfacebased neurophysiologic tests. The future scope of this

study is to build a low cost EEG device for various clinical

and pre-clinical applications with specic emphasis to

measure the effect of cognitive action on human brain.

Keywords:Cognitive Assessment, EEG, Brain,

Neurophysiologic Tests, EEG Device, Brain Computer

Interface.

1. INTRODUCTION

In different cognitive studies, brain

waves have their own importance and use-ful-ness for the estimation of variations in cogni-tive states parameters in study of stress, work-load, emotion,neural activities, neuro-logicaldisorders etc(Hamid at all, 2010; Lisetti and

Nasoz, 2004; Lisetti and Nasoz, 2004; Knollat all, 2011). In view of psychophysiology,the most suggested way of experimentationis based on cognitive test batteries (Gualt-ieri and Johnson, 2006; Ladner, 2008)which

bias subjects towards the aim of experimentalprotocol in order to get the accurate and goodresults, however the tests must be distinct fordifferent cases and properly investigated by

psychologists for best suitability.Neurophysiologic tests (Schuhfried at

all, 1985) are designed to investigate a vari-ety of cognitive functioning, including atten-tion, memory, language, speed of information

processing and executive functions, which areimportant for aimed behavior. Through neuro-

physiologic testing, a neurophysiologist canmake conclusions about underlying brain func-tion. As Neurophysiologic testing play a lessessential role in localization of brain abnor-malities, clinical neurophysiologist found newuses for their knowledge and skills. By iden-tifying which cognitive abilities are preservedor impaired in subjects with brain illness orin-jury, neurophysiologists can declare howwell subjects will respond to various forms oftreatment or rehabilitation. Although patternsof test scores serve as example proles of cog-nitive weakness and strength, neuro-physiol-

ogists can also learn an excellent deal aboutsubjects by observing how they come near toa particular test.

While neurological examination CT,MRI, EEG and PET (Dietrich and Kanso,2010) scans look at the structural, physical,and metabolic condition of the brain, the neu-ropsychological examination is the only wayto formally assess brain function. Neu-ropsy-chological tests extend over the range of cogni-tive processes from simple motor performanceto problem solving and complex reasoning.

Among all these techniques, EEG is thebest neuroimaging technique taking all of reli-ability, cost effectiveness, temporal resolution,

EEG INTERFACE MODULE FOR COGNITIVE ASSESSMENT

THROUGH NEUROPHYSIOLOGIC TESTS

MSc Kundan Lal Verma, Department of Electronics, DDU Gorakhpur University, Gorakhpur, India

E-mail: [email protected]

MSc Amit Kumar Jaiswal, Cognitive Brain Lab, National Brain Research Centre, Gurgaon, India


Dr. Manish Mishra, Department of Electronics, DDU Gorakhpur University, Gorakhpur, India


Dr. Anil Kumar Shukla, Amity Institute of Telecom Engineering & Management, Amity University, Noida, India


Received: September, 21.2014.

Accepted: October, 31.2014.

Original Article

UDK 621.8.082.9:612.82

616.8-073

Corresponding Author

MSc Kundan Lal Verma, Department of Electronics,DDU Gorakhpur University, Gorakhpur, India

mailto:klv.elect%40gmail.com?subject=mailto:akjaiswal%40nbrc.ac.in%20?subject=mailto:mmanishm%40yahoo.com?subject=mailto:akshukla2%40amity.edu?subject=mailto:klv.elect%40gmail.com?subject=mailto:klv.elect%40gmail.com?subject=mailto:akshukla2%40amity.edu?subject=mailto:mmanishm%40yahoo.com?subject=mailto:akjaiswal%40nbrc.ac.in%20?subject=mailto:klv.elect%40gmail.com?subject=


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data portability and system mobility perspec-tives in consideration so it is majorly beingused for BCI applications.

2. GENERAL STRUCTURE

The general structure of a BCI (BrainComputer Interface) is following:

A. Signal AcquisitionThe EEG signals are obtained from dif-

ferent regions of scalp through non-invasiveEEG channels. After that, the signal is ampli-ed and sampled for further analysis proce-dures. Some of the famous EEG acquisitionsystems are Neuroscan, EGI geodesic, Brain-Vision, RMS maximus, Biopac, Emotiv etc.

B. Pre-ProcessingIt is necessary to recover original EEG

signal for analysis from acquired signal (am-plied/modulated) via various methods such asnormalization, ltration and transfor-mation.

The power of the scalp EEG can varybetween different subjects due to several fac-tors, including also anatomical character-istics. For this reason, it is necessary to have away to account for differences in broad-band

power across subjects. This can be achieved

with different normalization approaches.To remove linear trends, it is often de-sirable to high-pass lter the data. For powerline noise removal we use notch lter. fordifferent noise frequency bands we can useappropriate band stop lters. Some of themain pre-processing methods/tools are nor-malization, ltering, wavelet, ICA, PCA, etc(Lakshmi, 2014).

C. Feature ExtractionWe cant consider the huge time domain

data points of EEG to operate a microcon-

troller/processor to perform the desired ac-tion so we select few appropriate features ofthe data like power, energy, entropy, fractaldimension etc. This feature is calculated for axed time segment and fed to processor forclassication. For example, suppose we use32 channel EEG system with 1000 Hz sam-

pling rate for BCI operation and power as fea-ture. If we x one second data segment, it willselect blocks of 1000 data points during dataacquisition and will operate queuing for pro-cessing. The Fourier transform is also widely

used in the applications of spectrum analysisbecause it takes a time domain discrete signaland transforms into frequency domain discrete

signal. Hilbert transform is also a very usefultransform in EEG feature extraction. Some ofthe most common features are power, energy,nor-malized power, entropy, amplitude,latency, fractal dimension, sample entropy,Largest Lyapunov exponent etc (Yin and Cao,2011; Napoli and Barbe, 2012; Tonga andBezerianos; 2002).

D. ClassicationClassiers are used for classifying the

pattern/class of the features extracted fromthe segments. Classiers may be supervisedor unsupervised. Supervised means that it isdesigned with some dened parameters andtrained with some data sets which is going to

be classied while unsupervised classiersare just like for bifurcation. The extracted fea-

tures are fed through queuing to the classierand send the class with most matching patternthen the respective task is performed. Classi-ers are also considered as trained machines.The Machine learning deals with programsthat learn from experience, i.e. programs thatimprove or adapt their performance on a cer-tain task or group of tasks over time. Machinelearning is training a machine to separate thedata given for testing on basis of data givenfor testing based on certain features. A clas-sier can be considered as a class/dimension

reducer. Some most common classiers areANN, kNN,SVM, k-means clustering, GMM,HMM etc (Mohandas and Gerropati, 2003;Lotte, 2007; Sulaiman at all, 2011; Cortes andVapnik, 1995).

ANN (Articial Neural Network)Articial neural networks are super-

vised classiers which need to train beforeoperation. They are developed on the basis ofreal neural networks underlying brain. Arti-cial neural networks are used for pattern recog-nition; data management and learning process

similar to brain. They are made up of articialneurons which give the concept of biologicalneurons and accept a number of inputs. Eachinput layer connected by weighted synapses.A neuron also has a specic threshold value. Ifthe sum of the weights is greater than thresh-old value, the neuron stimulated. The activa-tion function gives output of the neuron whichwill be the result of the problem and can befed as input for the other neuron. A number ofneurons are connected together to execute anarticial neural network which is arranged on

different layers. A neural network divided into input layer (which takes the values of in-putvariables) and output layer (the predic-tions of


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the result) and hidden layers of neurons, whichplay important role in the network as hiddenfunctioning takes place. The gure 1 gives aschematic of an articial neural network.

Figure 1. ANN Classier

kNN (k-Nearest Neighborhood)k-Nearest Neighborhood is also a type of

supervised learning. kNN is moderate straightforward classier and data are classied on the

basis of class of their nearest neighbors. It isoften useful to take more than one neighborinto account so the tech-nique is referred toas k-Nearest Neighbor (k-NN) classicationwhere k nearest neighbors are utilized in deter-mining the class. Since the training examplesare needed at run time, i.e. they need to bein memory at run time; it is sometimes also

called memory based classication. Becauseinduction is delayed to run time, it is con-sidered as lazy learning technique. Becauseclassication is directly based on the trainingan example that is why also called example-

based classi-cation or case-based classica-tion. A sim-ple illustration of kNN is shownin gure 2.

Figure 2. k-NN Classier

SVM (Support Vector Machine)It is an example of supervised machine

learning. The support vector machine (SVM)algorithm is probably the most widelyused kernel learning algorithm. It achieves

relatively robust pattern recognition perfor-mance using well established concepts in opti-mization theory. The main plan of the trainedSVM algorithm is to select new data positionin a category. The svmclassify function classi-es each row of data in sample using the infor-mation conveyed in the structure of supportvector machine classier svmstruct, createdusing function svmtrain. The gure 3 shows asimple structure of SVM.

Figure 3. SVM Classier

(GMM) Gaussian Mixture ModelIt is an example of unsupervised learn-

ing. Gaussian mixture models are the com-bination of multivariate normal densitycomponents. In GMM soft data clusters areassigned by choosing the component thatmaximizes the posterior probability. GMM

may be more suitable than k-means cluster-ingwhen clusters of different sizes having corre-lation between them. Following gure showsthe clustering by GMM.

Figure 4. GMM Classier

The overall EEG signal processing stepsare shown in gure 5.


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Figure 5. EEG signal processing steps

3. THE DEVICE MODULE

The main motivation is to build a brain-

computer interface for neurophysiologicassessment of different parameters during var-ious cognitive biasing tests. As per results ofvarious studies, the most utilized and easiestway of measuring brain waves would be EEGto record potential difference across differentlocations on the scalp. Our attention is connedon implementation of two-stage amplicationand ltering circuits. Besides, we use the buil-tin analog to digital conversion functionalityof the microcontroller in order to give goodquality digital signal as result. The optoislated

universal asynchronous receiver/transmittersends the digital values from ADC over USB/UART to a PC/Laptop unit connected to themicro-processor or microcontroller. The PC/Laptop executes all the preprocessing, trans-for-mation, feature extraction, machine learn-ing and task performing algorithms on C/MATLAB/SciLab/Python/Simulink or other

platform (Sharma and Gobbert, 2010) usingfew microprocessors and microcontrollers.

The structure of the device proposedconsists of an amplier circuitry consistingof an instrumentation amplier (the common-

mode noise ratio is evaluated using a right-legdriver attached to the subjects ear lobe or mas-toid, along with an operational amplier andsome lters (for removal of DC offsets, 50 Hz

powerline interference, and other noise arti-facts). In the next step, the signal passes to themicroprocessor/microcontroller unit, where itis digitized via an analog to digital converter.

Now, it is send over an USB/UART connec-tion to a PC/Laptop. The PC/Latop unit then

per-forms signal processing in MATLAB/Cand is procient to output the nal results

(brain wave parameters energy, power spectraldensity, root mean square value, entropy etc.for different bands alpha, beta, gamma etc.) to

the user. The other alternatives of MATLABare GNU Octave, Sage, FreeMat, R etc. butdue to some additional features such as fast

prototyping, more functions, concise codingand sufcient documentation MATLAB ismore preffered (Verma at all, 2012). A func-tional block dia-gram of the overall structureis shown in gure 6.

Figure 6. Device Module Schematic

4. CONCLUSIONS

This study concludes the requirementand present scenario of BCI development.It includes the implementation of a low cost

device to access the different cognitive pa-rameters like stress, workload, emotion,neural activities, neurological disorders etc. invarious cases as per described by neurophysi-ologic tests. The BCIs may play very criti-cal role in rehabilitation as well as cognitiveenhancement. It also presents a general struc-ture and module for a low cast BCI systemdevelopment.

Conict of interestsAuthors declare no conict of interest.

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