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2. Activation Modeling and Classification of Voluntary and Imagery Movements From the Prefrontal fNIRS Signals

Author

Md. Asadur Rahman, Mohd Abdur Rashid, Mohiuddin Ahmad, Anna Kuwana, and Haruo Kobayashi

Subjects
Voluntary movement, imagery movement, hemodynamic responses, prefrontal cortex, functional near-infrared spectroscopy, activation localization, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The trends in movement-related functional activity measurement for brain-computer interface (BCI) are mostly associated with the central lobe of the brain. This consideration may be a faulty approach for the paralyzed patient. This limitation demands an alternative approach for movement-related BCI. For the first time, we propose the prefrontal hemodynamics for implementing movement-related BCI. This paper aims to model the activation pattern and the classification performances of the prefrontal hemodynamics regarding the movement-related events. Utilizing functional near-infrared spectroscopy (fNIRS) the changes in the concentration of the oxidized hemoglobin and deoxidized hemoglobin regarding voluntary and imagery movements are acquired. With necessary preprocessing, the fNIRS signals are statistically analyzed to localize the most significant activated regions regarding the applied stimuli. The experiment shows that movement-related events have a strong correlation with the prefrontal hemodynamics. The patterns of the movement-related hemodynamics are modeled by polynomial regression and used to classify the voluntary and imagery events based on the maximum similarity approach. The resulting classification accuracies are found promising that proves the effectiveness of the prefrontal fNIRS signal to be effective in movement-related brain functionality analysis.

Published
2020
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3. Emotional State Classification from MUSIC-Based Features of Multichannel EEG Signals

Author

Sakib Abrar Hossain, Md. Asadur Rahman, Amitabha Chakrabarty, Mohd Abdur Rashid, Anna Kuwana, and Haruo Kobayashi

Subjects
EEG signal, MUSIC, PSD, feature extraction, classification, emotion recognition, Technology, Biology (General), QH301-705.5
Abstract

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.

Published
2023
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