Paper Info
Title
A Study On Mental State Classification Using Eeg-Based Brain-Machine Interface
Abstract
This work aims to find discriminative EEG-based features and appropriate classification methods that can categorise brainwave patterns based on their level of activity or frequency for mental state recognition useful for human-machine interaction. By using the Muse headband with four EEG sensors (TP9, AF7, AF8, TP10), we categorised three possible states such as relaxing, neutral and concentrating based on a few states of mind defined by cognitive behavioural studies. We have created a dataset with five individuals and sessions lasting one minute for each class of mental state in order to train and test different methods. Given the proposed set of features extracted from the EEG headband five signals (alpha, beta, theta, delta, gamma), we have tested a combination of different features selection algorithms and classifier models to compare their performance in terms of recognition accuracy and number of features needed. Different tests such as 10-fold cross validation were performed. Results show that only 44 features from a set of over 2100 features are necessary when used with classical classifiers such as Bayesian Networks, Support Vector Machines and Random Forests, attaining an overall accuracy over 87%.
Year
DOI
Venue
2018
10.1109/IS.2018.8710576
2018 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS (IS)
Keywords
Field
DocType
EEG, brain-machine interface, machine learning, mental states classification
Pattern recognition,Computer science,Brain–computer interface,Support vector machine,Bayesian network,Artificial intelligence,Classifier (linguistics),Random forest,Cross-validation,Discriminative model,Electroencephalography
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Jordan J. Bird194.05
Luis Manso24811.81
Eduardo Parente Ribeiro3145.42
Anikó Ekart456462.28
Diego R. Faria59514.96