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Enhancing Deep-learning Classification For Remote Motor Imagery Rehabilitation Using Multi-subject Transfer Learning In Iot Environment.

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Enhancing Deep-learning Classification For Remote Motor Imagery Rehabilitation Using Multi-subject Transfer Learning In Iot Environment.

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Enhancing Deep-Learning Classification for Remote Motor Imagery Rehabilitation Using Multi-Subject Transfer Learning

Joharah Khabti^1,2, Saad AlAhmadi^1,2, Adel Soudani^1,2

¹College of Computer and Information Sciences (CCIS), King Saud University, Riyadh 11543, Saudi Arabia.

Sensors (Basel, Switzerland)

|January 8, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a multi-subject transfer learning framework for remote motor imagery (MI) training using brain-computer interfaces (BCIs). The approach enhances accuracy and efficiency for flexible rehabilitation in an IoT environment.

Keywords:

brain–computer interface (BCI)deep learning (DL)edge computing electroencephalogram (EEG)internet of things (IoT)motor imagery (MI)transfer learning (TL)

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Area of Science:

Neuroscience
Computer Science
Rehabilitation Engineering

Background:

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) show promise for motor rehabilitation via motor imagery (MI) tasks.
Current MI training necessitates physical attendance, limiting rehabilitation flexibility.
Remote MI training faces challenges in accurate task recognition, computation, and communication costs, especially with complex EEG data and subject-dependent variations.

Purpose of the Study:

To propose a multi-subject transfer-learning approach for an efficient motor imagery training framework.
To develop an IoT architecture integrating cloud/edge computing for enhanced system efficiency and reduced network resource usage.
To improve the accuracy and efficiency of remote MI training for rehabilitation.

Main Methods:

Implemented a multi-subject transfer-learning approach within an IoT architecture featuring cloud/edge computing.
Utilized deep learning classification (with and without channel selection) in the cloud.
Applied multi-subject transfer-learning classification at the edge node, experimenting with various transfer-learning strategies.

Main Results:

The proposed framework significantly enhanced average accuracy in both multi-subject and single-subject transfer-learning classification.
Three-subject transfer learning achieved up to 79.77% accuracy (FCNNA model without channel selection).
Transfer learning improved average accuracy by up to 6.55% (two-subject) and 12.19% (single-subject) compared to non-transfer learning methods.

Conclusions:

The developed framework offers a viable solution for remote motor imagery rehabilitation.
It provides accurate motor imagery task recognition while optimizing computational and communication resource usage.
This approach facilitates flexible and efficient rehabilitation through advanced BCI and IoT integration.