May 1, 2020 · In this study, a deep neural network model has been introduced to identify the exact objectives of the human brain by introducing temporal and spatial features.
A novel classification framework using multiple bandwidth method with optimized CNN for brain–computer interfaces with EEG-fNIRS signals · Mental arithmetic task ...
Deep Recurrent-Convolutional NeuralNetwork for Classification of ...
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In this study, a deep neural network model has been introduced to identify the exact objectives of the human brain by introducing temporal and spatial features.
Jul 20, 2022 · Deep learning (DL) methodologies have demonstrated fast and accurate performances in data processing and classification tasks across many biomedical fields.
May 12, 2020 · Bibliographic details on Deep recurrent-convolutional neural network for classification of simultaneous EEG-fNIRS signals.
Deep recurrent–convolutional neural network for classification of simultaneous EEG–fNIRS signals · Hamidreza Ghonchi,; Mansoor Fateh,; Vahid Abolghasemi, ...
May 28, 2024 · This paper presents the results of the investigations of a cohort of 26 students exposed to Guided Imagery relaxation technique and mental task workloads.
Sep 4, 2024 · Finally, integrating the fNIRS and EEG data using recurrence plots and CNN-LSTM models offers encouraging results for classification tasks [15].
This paper establishes a cyclic convolutional neural network based on the meshless method. This paper demonstrates an agent model of cyclic convolutional neural ...
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May 13, 2024 · The main focus is to classify the fNIRS signals acquired during human–computer interactions to identify the different brain activities. Another ...