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Enhancing Classification Accuracy with Integrated Contextual Gate Network: Deep Learning Approach for Functional

Jamila Akhter1, Noman Naseer1, Hammad Nazeer1

  • 1Department of Mechatronics and Biomedical Engineering, Air University, Islamabad 44000, Pakistan.

Sensors (Basel, Switzerland)
|May 25, 2024
PubMed
Summary

A novel deep learning algorithm, the integrated contextual gate network (ICGN), significantly improves accuracy in functional near-infrared spectroscopy brain-computer interface (fNIRS-BCI) systems for motor activity classification.

Keywords:
bidirectional long short-term memory (Bi-LSTM)brain–computer interfacing (BCI)deep learning (DL)functional near-infrared spectroscopy (fNIRS)integrated contextual gate network (ICGN)long short-term memory (LSTM)

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Brain-computer interface (BCI) systems translate brain signals into commands.
  • Deep learning (DL) algorithms enhance accuracy in fNIRS-BCI by automating feature extraction.
  • Traditional machine learning requires manual feature engineering, which DL bypasses.

Purpose of the Study:

  • To introduce and evaluate a novel deep learning algorithm, the integrated contextual gate network (ICGN).
  • To enhance classification accuracy in fNIRS-BCI systems for motor activity detection.
  • To compare the performance of ICGN against established DL models like LSTM and Bi-LSTM.

Main Methods:

  • Acquisition of a two-class hand-gripping motor activity dataset from 20 healthy participants.
  • Application of the proposed ICGN algorithm for feature extraction and classification.
  • Validation using an open-access three-class motor imagery dataset from 30 subjects.

Main Results:

  • The ICGN algorithm achieved a classification accuracy of 91.23 ± 1.60%.
  • This accuracy was significantly higher (p < 0.025) than LSTM (84.89 ± 3.91%) and Bi-LSTM (88.82 ± 1.96%).
  • ICGN demonstrated efficiency in classifying both two- and three-class motor tasks in fNIRS-BCI.

Conclusions:

  • The proposed ICGN algorithm offers superior performance for fNIRS-BCI motor activity classification.
  • ICGN effectively automates feature extraction and pattern recognition, outperforming existing DL models.
  • ICGN is a promising tool for advancing fNIRS-BCI applications requiring accurate classification of neural signals.