Motor Imagery Movement and Neurological Disorder Classification using Salient Features of EEG Signal

Abstract

Electroencephalography (EEG) measurement plays a significant role in the clinical and scientific research of brain studies. EEG signals are very important, particularly for classification and treatment of neurological diseases and brain computer interface (BCI) applications. The aim of this dissertation is to develop methods for the analysis and classification of different categories of motor imagery (MI) movements, epileptic EEG signals, and human alertness states. A novel method is also developed for continuous alertness monitoring. EEG signals of MI movements are classified for right hand and left hand (two class) and right hand, left hand, and feet (three class) movements. Nowadays, MI is a highly prescribed method for the disabled patients to give them hope to control machine or computer by interfacing with brain or mind. This dissertation proposes a classification method between imagery left and right hands movement using Daubechies wavelet of discrete wavelet transform (DWT) and Levenberg-Marquardt back propagation training algorithm of artificial neural network (ANN). DWT decomposes the raw EEG data to extract significant features that provide feature vectors precisely. ANN classifies the two class and three class trials data. Classification accuracy varies with respect to the subject. This method can be used to design a well-organized BCI system with better accuracy. Results from classifier can be used to design brain machine interface (BMI) for better performance that requires high precision and accuracy scheme. Neurological disorder i.e. epilepsy detection is enough time consuming and requires thorough observation to determine epilepsy type and locate the responsible area of the cerebral cortex. The dissertation proposes an effortless epilepsy classification method for epilepsy detection and investigates the classification accuracy of multiclass EEG signal during epilepsy. For accomplishing the proposed research work we use DWT to obtain responsible features to accumulate feature vectors. Afterward feature vectors are given in the input layer of the ANN classifiers to differentiate normal, interictal, and ictal EEG periods. Accuracy rate is calculated based on the confusion matrix. Proposed method can be utilized to monitor and detect epilepsy type incorporating with an alarm system. It is tiresome for human to concentrate constantly, though several works require continuous alertness like efficient driving, learning, etc. A practical method is applied to investigate the concentration state of human brain by EEG acquisition. This research work proposes continuous alertness state classification method based on two different types of mental tasks with respect to the resting state (resting with eyes open and eyes close). To conduct this research work, some participants were involved and they performed several tasks such as alphabet counting, virtual motor driving, resting with eyes open and eyes close. During the performances of the tasks, 9 channel EEG data has been acquired from their scalps. The data acquisition is performed by B-Alert (BIOPAC) system. The acquired data are filtered by IIR filter and responsible channels are selected by the statistical method. The features of the signals were extracted by using principal component analysis (PCA) and DWT algorithms. The alert states of our brain are classified by ANN. In addition, the specific relative power (RP) of the responsible frequency band of EEG signals is calculated for alertness monitoring. Within the RP range of resting and active state, a threshold value is proposed for monitoring the alertness state of the participants. This work will be helpful to classify the epileptic states with more accuracy as well as this works is also a well guide to classify the motor imagery movements. In addition, the proposed method based on continuous alertness monitoring will be remarkable approach to design machines for monitoring driving or learning.