Enhancing Cognitive Workload Classification Using Integrated LSTM Layers and CNNs for fNIRS Data Analysis

Enhancing Cognitive Workload Classification Using Integrated LSTM Layers and CNNs for fNIRS Data Analysis

Functional near-infrared spectroscopy (fNIRS) is employed as a non-invasive method to monitor functional brain activation by capturing changes in the concentrations of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR). Various machine learning classification techniques have been utilized...

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Main Authors: Mehshan Ahmed Khan, Houshyar Asadi, Mohammad Reza Chalak Qazani, Adetokunbo Arogbonlo, Siamak Pedrammehr, Adnan Anwar, Hailing Zhou, Lei Wei, Asim Bhatti, Sam Oladazimi, Burhan Khan, Saeid Nahavandi
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Computers
Subjects:
functional near-infrared spectroscopy (fNIRS)
deep learning
machine learning
n-back tasks
cognitive load
Online Access:https://www.mdpi.com/2073-431X/14/2/73
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