Enhancing EEG-Based MI-BCIs with Class-Specific and Subject-Specific Features Detected by Neural Manifold Analysis

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 18; p. 6110
• Main Authors: Frosolone, Mirco, Prevete, Roberto, Ognibeni, Lorenzo, Giugliano, Salvatore, Apicella, Andrea, Pezzulo, Giovanni, Donnarumma, Francesco
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.09.2024; MDPI
• Subjects: Accuracy; Algorithms; Automatic classification; Brain research; Brain-Computer Interfaces; Classification; Datasets; Deep learning; EEG-based BCI; Electroencephalography; Electroencephalography - methods; Electronic data processing; Fourier transforms; Humans; Image processing; Imagination - physiology; Methods; motor imagery BCI; neural manifold analysis; Neural networks; Signal processing; Signal Processing, Computer-Assisted; Technology application; transfer learning; Wavelet transforms; Italy; EEG-based BCI; transfer learning; motor imagery BCI; neural manifold analysis
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24186110

This paper presents an innovative approach leveraging Neuronal Manifold Analysis of EEG data to identify specific time intervals for feature extraction, effectively capturing both class-specific and subject-specific characteristics. Different pipelines were constructed and employed to extract distinctive features within these intervals, specifically for motor imagery (MI) tasks. The methodology was validated using the Graz Competition IV datasets 2A (four-class) and 2B (two-class) motor imagery classification, demonstrating an improvement in classification accuracy that surpasses state-of-the-art algorithms designed for MI tasks. A multi-dimensional feature space, constructed using NMA, was built to detect intervals that capture these critical characteristics, which led to significantly enhanced classification accuracy, especially for individuals with initially poor classification performance. These findings highlight the robustness of this method and its potential to improve classification performance in EEG-based MI-BCI systems.