Keywords : EEG


Diagnosing Mental Disorders based on EEG Signal using Deep Convolutional Neural Network

Ranjani M; Supraja P

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue 7S, Pages 132-137
DOI: 10.47392/irjash.2021.222

Suicides are on the rise all across the world, and depression is a prevalent cause. As a result, effective diagnosis and therapy are required to lessen the symptoms of depression and anxiety. An electroencephalogram (EEG) is a device that measures and records electrical activity from the brain. It can be used to generate a precise assessment on the severity of depression and anxiety. Previous research has shown that EEG data and deep learning (DL) models can be used to diagnose various psychiatric disorders. As a result, this paper offers DeepNet, a DL-based convolutional neural network (CNN) for identifying EEG data from depressed, anxiety and healthy people. This study examines DeepNet's performance in two trials, namely the subject wise split and the record wise split. DeepNet's results have an accuracy of 0.9837, and when record wise split data is used, the area under the receiver operating characteristic curve (AUC) is 0.989.

EEG based Emotion Recognition and Classification: a Review

Ramprasad Kumawat; Manish Jain

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue ICITCA-2021 5S, Pages 1-10
DOI: 10.47392/irjash.2021.131

Emotion plays a vital role in medical research and interpersonal communication. Essentially feeling can be communicated verbally like discourse or non-verbally like outward appearance and physiological signals. A human emotion is complex physiological state which involves a physiological response, a person’s experience and behavioral change. EEG measures electric current that are generated due to neuronal activities in the human brain. This paper provides an overview of comparative study of various techniques of emotion recognition from EEG signals. Our analysis is based on extracted features and classification methods of emotion recognition. We intended that, this study will be useful for newly researchers those entering in the field of emotion recognition.

Future of EEG Based Hybrid Visual Brain Computer Interface Systems in Rehabilitation of People with Neurological Disorders

Deepak Kapgate

International Research Journal on Advanced Science Hub, 2020, Volume 2, Issue 6, Pages 15-20
DOI: 10.47392/irjash.2020.31

Brain-computer interfaces (BCIs) are technologies that make it possible for humans to control external devices merely using their cortical potentials rather than normal output pathways such as muscles or peripheral nerves. BCIs present a hope towards restoration of independence for people affected by neurological disorders or disable individuals. Hybrid visual BCI (V-BCI) i.e. BCIs those are using two or more types of visual evoked potential for its operation, are providing promising results than other all BCI systems types. Over past two decades research and development in hybrid V-BCI systems have grown tremendously. Recently lot of efforts has been placed to make laboratory validated hybrid V-BCI systems to work in real life applications for disables. In this paper we argue on possible futuristic applications of hybrid V-BCI systems and its clinical relevance. We will present existing restrictions of hybrid V-BCI technology and its futuristic expectations.