A Review on Classification and Feature Extraction Techniques for EEG Signal Processing

In the current world, many people are affected by mental diseases. Depression is the most common mental illness. It is a vital cause of sickness. Suicide is the leading cause of depression. Clinical diagnosing symptoms is not a reliable assessment of mental depression disease reported by doctors. In modern techniques, Electroencephalogram (EEG) based emotion recognition is a new arena in this area with challenging issues concerning the induction of emotion state diagnosis. In this paper, we conclude the different technology for mental diagnosis analysis, which helps further clinical assessment. The techniques are eye gaze signal tracking, facial expression signal tracking, and Brain-Computer interface technologies between humans and machines. EEG signal for tag relevance assessment and effective neuromodulator therapies etc. It is based on statistics tools like Artificial Neural Networked models for brain-computer interface and proper detection of emotion classifications reorganization etc