NeuroAssist: Enhancing Cognitive-Computer Synergy with Adaptive AI and Advanced Neural Decoding for Efficient EEG Signal Classification

TL;DR

NeuroAssist introduces an advanced hybrid neural network system that combines NLP, LSTM, SNNs, and DQNs to improve EEG signal classification for brain-computer interfaces, achieving over 99% accuracy.

Contribution

This paper presents a novel hybrid neural network architecture integrating multiple AI models for adaptive EEG analysis in BCI systems, surpassing traditional methods.

Findings

Achieved 99.17% classification accuracy on benchmark datasets.

Demonstrated improved flexibility and responsiveness in EEG-based control.

Enhanced decision-making through integrated deep reinforcement learning.

Abstract

Traditional methods of controlling prosthetics frequently encounter difficulties regarding flexibility and responsiveness, which can substantially impact people with varying cognitive and physical abilities. Advancements in computational neuroscience and machine learning (ML) have recently led to the development of highly advanced brain-computer interface (BCI) systems that may be customized to meet individual requirements. To address these issues, we propose NeuroAssist, a sophisticated method for analyzing EEG data that merges state-of-the-art BCI technology with adaptable artificial intelligence (AI) algorithms. NeuroAssist's hybrid neural network design efficiently overcomes the constraints of conventional EEG data processing. Our methodology combines a Natural Language Processing (NLP) BERT model to extract complex features from numerical EEG data and utilizes LSTM networks to handle temporal dynamics. In addition, we integrate spiking neural networks (SNNs) and deep Q-networks (DQN) to improve decision-making and flexibility. Our preprocessing method classifies motor imagery (MI) one-versus-the-rest using a common spatial pattern (CSP) while preserving EEG temporal characteristics. The hybrid architecture of NeuroAssist serves as the DQN's Q-network, enabling continuous feedback-based improvement and adaptability. This enables it to acquire optimal actions through trial and error. This experimental analysis has been conducted on the GigaScience and BCI-competition-IV-2a datasets, which have shown exceptional effectiveness in categorizing MI-EEG signals, obtaining an impressive classification accuracy of 99.17%. NeuroAssist offers a crucial approach to current assistive technology by potentially enhancing the speed and versatility of BCI systems.