TASMANIAN DEVIL HUNTING OPTIMIZATION ENABLED DEEP MAXOUT NETWORK FOR BRAIN ACTIVITY DETECTION BASED ON MOTOR IMAGERY EEG SIGNALS

Abstract

Brain activity leads to devastating effects on life which may lead to the loss of human lives. It can be detected at early stages to save human life. An electroencephalogram (EEG) is a test that will detect abnormalities in the brain wave. Electrodes are applied to the scalp during an EEG. These are tiny metal disks connected by slender wires. They pick up microscopic electrical charges produced by the brain’s cell activity. The results of an EEG reveal alterations in brain activity that may be helpful in the diagnosis of various brain disorders, particularly epilepsy and other conditions that result in seizures. In this research, a novel approach termed Tasmanian Devil Hunting Optimization-Deep Maxout Network (TDHO-DMN) is devised for brain activity detection based on motor imagery EEG signals. Initially, the input EEG signal obtained from the dataset is subjected to the signal pre-processing phase. Here, the input signals are pre-processed for denoising utilizing the Gaussian Filter. After that, the pre-processed signal is allowed for the feature extraction to extract the suitable feature vectors like amplitude modulation spectrum (AMS), frequency-based features and statistical features. Then, extracted features are fed to data augmentation which is carried out utilizing the oversampling technique. Finally, brain activity detection is accomplished by the Deep Maxout Network (DMN), which is trained by the Tasmanian Devil Hunting Optimization (TDHO) algorithm. TDHO is formed by the combination of Tasmanian Devil Optimization (TDO) and Deer Hunting Optimization Algorithm (DHOA). The performance evaluation of the proposed TDHO_DMN is analyzed using two benchmark datasets, where the proposed TDHO_DMN approach obtained a better performance in terms of accuracy, sensitivity and specificity of 90.70%, 91.00% and 91.40%, respectively.