Motor Imagery Tasks Based Electroencephalogram Signals Classification Using Data-Driven Features

Kardam, Vikram Singh; Taran, Sachin; Pandey, Anukul

doi:10.1016/j.neuri.2023.100128

Cited by 6 publications

(1 citation statement)

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“…The BF-Tree algorithm continues the best-first search and pruning procedure up until a stopping criterion is met, such as reaching a certain depth or when further splitting does not provide significant improvement in the evaluation metric. BF-Tree has been used in a wide range of EEG-based mental state classification studies in the literature [ 71 – 73 ].…”

Section: Proposed Methodologymentioning

confidence: 99%

A machine learning based depression screening framework using temporal domain features of the electroencephalography signals

Khan,

Umar Saeed,

Frnda

et al. 2024

PLoS ONE

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Depression is a serious mental health disorder affecting millions of individuals worldwide. Timely and precise recognition of depression is vital for appropriate mediation and effective treatment. Electroencephalography (EEG) has surfaced as a promising tool for inspecting the neural correlates of depression and therefore, has the potential to contribute to the diagnosis of depression effectively. This study presents an EEG-based mental depressive disorder detection mechanism using a publicly available EEG dataset called Multi-modal Open Dataset for Mental-disorder Analysis (MODMA). This study uses EEG data acquired from 55 participants using 3 electrodes in the resting-state condition. Twelve temporal domain features are extracted from the EEG data by creating a non-overlapping window of 10 seconds, which is presented to a novel feature selection mechanism. The feature selection algorithm selects the optimum chunk of attributes with the highest discriminative power to classify the mental depressive disorders patients and healthy controls. The selected EEG attributes are classified using three different classification algorithms i.e., Best- First (BF) Tree, k-nearest neighbor (KNN), and AdaBoost. The highest classification accuracy of 96.36% is achieved using BF-Tree using a feature vector length of 12. The proposed mental depressive classification scheme outperforms the existing state-of-the-art depression classification schemes in terms of the number of electrodes used for EEG recording, feature vector length, and the achieved classification accuracy. The proposed framework could be used in psychiatric settings, providing valuable support to psychiatrists.

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Section: Proposed Methodologymentioning

confidence: 99%