Symmetric feature-based transfer learning for brain–computer interface

What is it about?

In this research study, a feature-based homogenous transfer learning approach was explored for the classification domain to reduce the training and calibration time for the fNIRS-based BCI systems. We evaluated the validity and viability of transfer learning for the fNIRS-based BCI systems under the following different assumptions. First, the transfer learning efficiently transferred the source domain knowledge to the target domain and required reduced training iterations for deep learning models. Second, transfer learning minimizes the need for a large amount of data needed for training deep learning models for the target domain. We used 16 subjects to train the CNN network and named it a ‘learned CNN’ network that learns the source domain knowledge of the n-back dataset. Further, we split the remaining ten subjects into two groups, i.e., the control and baseline group. The control group is trained with the learned CNN network and baseline with a randomly initialized CNN network, and their accuracies are compared using statistical analysis. The results suggested that applying the proposed feature-based transfer learning algorithms could achieve the maximum saturated accuracy sooner than the baseline group, which reduces the training time. The proposed transfer learning method also outperformed the averaged accuracy achieved using the novel learned CNN model (94.52%) over the traditional CNN model (68.94%) by 25.58%. Thus, the proposed transfer learning methodology for fNIRS is a promising solution for both the problems of increased training iterations for deep learning models and limited training datasets for BCI.

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Photo by Possessed Photography on Unsplash

Photo by Possessed Photography on Unsplash

Why is it important?

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.

Perspectives