Optimized EEG based mood detection with signal processing and deep neural networks for brain-computer interface

Abstract

Electroencephalogram (EEG) is a very promising and widely implemented procedure to study brain signals and activities by amplifying and measuring the post-synaptical potential arising from electrical impulses produced by neurons and detected by specialized electrodes attached to specific points in the scalp. It can be studied for detecting brain abnormalities, headaches, and other conditions. However, there are limited studies performed to establish a smart decision-making model to identify EEG's relation with the mood of the subject. In this experiment, EEG signals of 28 healthy human subjects have been observed with consent and attempts have been made to study and recognise moods. Savitzky-Golay band-pass filtering and Independent Component Analysis have been used for data filtration. Different neural network algorithms have been implemented to analyze and classify the EEG data based on the mood of the subject. The model is further optimised by the usage of Blackman window-based Fourier Transformation and extracting the most significant frequencies for each electrode. Using these techniques, up to 96.01% detection accuracy has been obtained.

Toward Improved Classification of Perceived Stress Using Time Domain Features

“…Among physiological sensors, EEG signals represent the different states of mind [17]. It is a promising and widely used technology for studying brain activity [18]. When it comes to assessing perceived stress, the publicly available EEG datasets are quite limited.…”