EEG Based Patient Monitoring System for Mental Alertness Using Adaptive Neuro-Fuzzy Approach

Abstract

Recent electrophysiological studies support command-specific changes in the electroencephalography (EEG) that have promoted their intensive application in the noninvasive brain computer interfaces (BCI). However, EEG is plagued by a variety of interferences and noises, thereby demanding better accuracy and stability for its application in the neuroprosthetic devices. Here we investigate wavelets and adaptive neuro-fuzzy classification algorithms to enhance the classification accuracy of cognitive tasks. Using a standard cognitive EEG dataset, we demonstrate improved performance in the classification accuracy with the proposed system. 

EEG based Brain Alertness Monitoring by Statistical and Artificial Neural Network Approach

“…Sleeping state begins with the activation of neurons and brain inhibition. The transformation of awaking or alertness state to unconscious or drowsiness state is described by certain rhythmic changes [8][9][10]: (i) decreased the beta rhythmic (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) activity, (ii) increase in alpha rhythm activity (8)(9)(10)(11)(12)(13) but best observable while resting by eyes closed; and (iii) increased theta rhythm activity (4-8 Hz) if consequently alpha rhythm decreased.…”

“…In another research, the article proposed a method to increase the performance of mental task using WT and EMD feature extraction method [26]. There are some other works those were performed special investigations such as a BCI application of patient monitoring by EEG signal of mental alertness [27], mental fatigue or workload comparing with alertness [28] and a drowsy driving monitoring technique [29]. These methods are very complicated and several features with classifier are used to determine alertness/active state.…”