Abstract:
Electro encephalography (EEG) can b e used to classify attempted hand movements
of Spinal Cord Injured (SCI) patients for improving their quality of life. EEG
classifiation with Brain-Computer Interface (BCI) allows individuals who are
suffring from the most severe motor disabilities to control and direct
electromechanical devices. Practical applications of BCI system require improved
classifiation p erformance for attempted hand movements of SCI patients. This
research aims to develop a hybrid CNN-LSTM architecture for multichannel EEG
signal classifiation, optimize its hyp erparameters, and validate p erformance metrics
for improved classifiation p erformance. EEG data acquired from SCI patients go
through fitration, downsampling, and artifact removal, followed by the generation of
Time-Frequency Representation (TFR) of EEG data. Spatial enco ding is done by
arranging TFR data in a 2D array corresp onding to spatial layout of electro des.
Spatial enco ded TFR data is then fed to CNN-LSTM (Convolutional Neural
Network – Long Short Term Memory) N etwork to obtain the fial classifiation
output. Sp ectral, spatial, and temp oral information is vital in EEG classifiation.
Novelty in the design of Hybrid CNN-LSTM network architecture is that it can
learn to extract sp ectral, spatial, and temp oral information and then use these
learned information to improve fial classifiation p erformance. Hybrid CNN-LSTM
architecture achieved a classifiation accuracy of 92.36% using 10% of the dataset
for training and 90% of the dataset for testing. This result shows 47.363% increased
classifiation accuracy as compared to related study while also having improved
generalizability. H ybrid CNN-LSTM network for EEG classifiation is able: to deal
with artifacts in EEG data without signifiant loss in classifiation p erformance; to
extract sp ectral information, spatial information, and temp oral information of
valuable neural impulses from EEG data. The steps of EEG classifiation used in
this research can b e used not only for attempted movements of SCI patients but also
for other neurological diseases, neuroscience applications, mental workload,
neuromarketing, and biometrics.
