Classifying the Autism and Epilepsy Disorder Based on EEG Signal Using Deep Convolutional Neural Network (DCNN)

In the field of Brain-Computer Interface (BCI), usage of Electroencephalogram (EEG) signals provides various applications in many fields. EEG signals investigation is necessary due to an effective way of determining neurological brain disorders. These disorders include Epileptic and autism in the brain structures which are identified by the abnormal synchronous electrical discharge of the neuronal activities. The multichannel EEG signals are recorded these abnormal electrical events which can be positioned on the scalp of the brain. The aim of this research work is to classify various patterns of EEG recording samples namely autistic, epilepsy and normal using the deep learning algorithm. In order to remove the artifacts from EEG dataset, the Independent Components Analysis (ICA) technique is used to minimization the noise which is segmented and filtered using an elliptic band-pass filter. Next, EEG signal transforms to Power Spectrum Density Energy Diagrams (PSDEDs). Therefore, the techniques of feature extraction can transform the signals into various feature vectors which it automatically extracts features from the PSDED by applying Deep Convolutional Neural Networks (DCNN) and by making use of the Fully Connected (EC) layer the signals can be classified as autistic, epilepsy and normal. The classification accuracy created by the suggested DCNN model is achieved 80% for training data set validation.