Novel fNIRS study on homogeneous symmetric feature-based transfer learning for brain\u2013computer interface

Khurram Khalil,Umer Asgher,Yasar Ayaz

doi:10.1038/s41598-022-06805-4

Khurram Khalil, Umer Asgher + Show 1 more

Open Access

https://doi.org/10.1038/s41598-022-06805-4

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Abstract

The brain–computer interface (BCI) provides an alternate means of communication between the brain and external devices by recognizing the brain activities and translating them into external commands. The functional Near-Infrared Spectroscopy (fNIRS) is becoming popular as a non-invasive modality for brain activity detection. The recent trends show that deep learning has significantly enhanced the performance of the BCI systems. But the inherent bottleneck for deep learning (in the domain of BCI) is the requirement of the vast amount of training data, lengthy recalibrating time, and expensive computational resources for training deep networks. Building a high-quality, large-scale annotated dataset for deep learning-based BCI systems is exceptionally tedious, complex, and expensive. This study investigates the novel application of transfer learning for fNIRS-based BCI to solve three objective functions (concerns), i.e., the problem of insufficient training data, reduced training time, and increased accuracy. We applied symmetric homogeneous feature-based transfer learning on convolutional neural network (CNN) designed explicitly for fNIRS data collected from twenty-six (26) participants performing the n-back task. The results suggested that the proposed method achieves the maximum saturated accuracy sooner and outperformed the traditional CNN model on averaged accuracy by 25.58% in the exact duration of training time, reducing the training time, recalibrating time, and computational resources.

Highlights

The brain–computer interface (BCI) provides an alternate means of communication between the brain and external devices by recognizing the brain activities and translating them into external commands
Deep learning (DL) algorithms have been vigorously applied in different BCI studies such as an artificial neural network (ANN)[24,25], convolutional neural networks (CNN)[26,27], deep belief network (DBN)[28], long short-term memory (LSTM)[29,30], and cascade CNN-LSTM31
A feature-based homogenous transfer learning approach was explored for the classification domain to reduce the training and calibration time for the functional Near-Infrared Spectroscopy (fNIRS)-based BCI systems

Summary

Introduction

The brain–computer interface (BCI) provides an alternate means of communication between the brain and external devices by recognizing the brain activities and translating them into external commands. Using the BCI systems out of the laboratory needs to address several challenges such as robust signal acquisition, extracting required information from raw brain signals, and accurate control or command generation through data classification[8,9] Another challenge hindering the BCI systems is the need for lengthy recalibration due to the high dimensionality and low signal-to-noise ratio (SNR) of EEG and fNIRS s ignals[10]. It is difficult to approximate probability distributions of the feature vectors from low SNR signals, mostly in the case of machine learning (ML) algorithms, where only a few trials are performed for multi-dimensional brain signals All these factors lead to the poor performance of trained classifiers on new session data. This study’s major takeaway is that optimization obtained through transfer learning is superior to traditional DL network training

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