Multiclass functional Near-Infrared Spectroscopy (fNIRS) signal classification has become a convenient way for optical brain-computer interface. fNIRS signal classification with high accuracy is a challenging assignment while the signals are produced by means of voluntary and imagery movements of the same limb. Since the activation in time and space of voluntary and imagery movement show a similar pattern, the classification accuracy by the conventional shallow classifiers cannot reach an acceptable range. This paper proposes an accuracy improvement approach with the use of Convolutional Neural Networks (CNNs). In this work, voluntary and imagery hand movements (left hand and right hand) were performed by several participants. These four-class signals were acquired utilizing fNIRS devices. The signals were separated based on the tasks and filtered. With manual feature extraction, the signals were classified by support vector machine and linear discriminant analysis. The automatic feature extraction and classification mechanism of the CNN were applied to the fNIRS signals. From the results, it was found that CNN improves the classification accuracy to an acceptable range, which has not been achieved by any convolutional network.

Electroencephalogram (EEG)-based emotion recognition is a computationally challenging issue in the field of medical data science that has interesting applications in cognitive state disclosure. Generally, EEG signals are classified from frequency-based features that are often extracted using non-parametric models such as Welch’s power spectral density (PSD). These non-parametric methods are not computationally sound due to having complexity and extended run time. The main purpose of this work is to apply the multiple signal classification (MUSIC) model, a parametric-based frequency-spectrum-estimation technique to extract features from multichannel EEG signals for emotional state classification from the SEED dataset. The main challenge of using MUSIC in EEG feature extraction is to tune its parameters for getting the discriminative features from different classes, which is a significant contribution of this work. Another contribution is to show some flaws of this dataset for the first time that contributed to achieving high classification accuracy in previous research works. This work used MUSIC features to classify three emotional states and achieve 97% accuracy on average using an artificial neural network. The proposed MUSIC model optimizes a 95–96% run time compared with the conventional classical non-parametric technique (Welch’s PSD) for feature extraction.