Open AccessPSD-Based Features Extraction For EEG Signal During Typing Task
Author(s) -
Wei Bin Ng,
A. Saidatul,
Yen Fook Chong,
I. Zunaidi
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/557/1/012032
Subject(s) - electroencephalography , linear discriminant analysis , pattern recognition (psychology) , computer science , artificial intelligence , speech recognition , channel (broadcasting) , laptop , signal processing , digital signal processing , psychology , telecommunications , psychiatry , computer hardware , operating system
Electroencephalograph (EEG) is an electrical field that generated by our brain incessantly. The EEG signal released by the brain is different when a people is performing different activities in their daily life. And such EEG signals consist complicated information that can be interpreted. The aims of this study is to analyse the specific EEG channels of a user when they are performing a typing task with laptop. Meanwhile, this research also aimed to verify the performance of the different sub frequency band which is alpha and beta to recognize the specified tasks. The frequency sampling was set at 1024 Hz and the impedance was kept below 5k ohm of each channels. The Truscan EEG (Deymed, Diagnostic, Czech Republic) device consists of 19 channels and only selected channels which is F3 and F4 is filtered through butterworth bandpass filter (1Hz-80Hz) in the pre-processing stage. Power Spectra Density was calculated by using Welch and Burg Method to extract the features from filtered data. K-Nearest Neighbour (KNN) classifier and Linear Discriminant Analysis (LDA) were used in classification. It is found that the combination of channel F3 and F4 for Alpha frequency using Welch method gives the highest accuracy which is 98.45%.