Highly Accurate EEG Signal Classification Using Multiple Feature Extraction and LSTM Networks

Deshmukh Deepika (Login required)
Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana, India

Gillala Rekha
Mahatma Gandhi Institute of Technology, Hyderabad, Telangana, India


Paper #9029 received 21 Oct 2023; revised manuscript received 22 Jan 2024; accepted for publication 23 Jan 2024; published online 3 Mar 2024.

DOI: 10.18287/JBPE24.10.010304

Abstract

Brain-computer interface (BCI) was first only thought of as a control channel for end users with simple disabilities, like those locked in a room. Nevertheless, the spectrum of BCI applications has significantly increased due to the multidisciplinary advancements made over the past ten years. Today’s BCI technology can combine this artificial output with muscle-based natural products, directly translating brain impulses into control signals. Therefore, combining several biological signals for in-the-moment communication can benefit a far bigger population than first anticipated. New generations of assistive devices could aid individuals with preserved residual functions. Electroencephalography (EEG) signals can effectively perform BCI with maximum accuracy. This work will implement a new multiple-feature extraction-based BCI with a deep convolutional neural network to progress accuracy and reduce system complexity. The EEG signal is extracted from a database and then preprocessed using de-noising and smoothing techniques. Extracting features from the signal, then concatenating the features that have been extracted. After that, the Long Short-Term Memory (LSTM) network is used to train the signals. A proper preprocessing technique will remove the artefacts from the captured EEG signal. Sensitivity evaluation metrics, such as sensitivity, specificity, accuracy, etc., will be evaluated to validate the training and testing performance. This work achieves 99.35% accuracy, 96.38% sensitivity, 99.18% specificity, and 99.21% precision. By combining a range of feature extraction methods, we have effectively harnessed the complementary strengths of these techniques, resulting in a more holistic representation of the underlying neural activities.

Keywords

brain-computer interfaces; electroencephalography (EEG); deep convolutional neural networks; multiple feature extraction; sensitivity evaluation

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