An Edge AI System-on-Chip Design with Customized Convolutional-Neural-Network Architecture for Real-time EEG-Based Affective Computing System

Huang, Yuan Ming; Wang, Kai-Yen; Ho, Yun-Lung; He, Chang-Yuan; Fang, Wai-Chi

doi:10.1109/biocas.2019.8919038

Cited by 8 publications

(4 citation statements)

References 3 publications

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“…Song et al (2018) designed the Dynamical Graph Convolution Neural Network (DGCNN) method for this categorization, and an average accuracy of 84.54% and 86.23% was obtained [10]. The use of the CNN method for classifying emotions into two categories was also proposed by Huang et al (2019). This approach produced an average accuracy of 84.5% and 83.7% for valence and arousal [37].…”

Section: -Related Workmentioning

confidence: 99%

“…The use of the CNN method for classifying emotions into two categories was also proposed by Huang et al (2019). This approach produced an average accuracy of 84.5% and 83.7% for valence and arousal [37]. Wardoyo et al (2022) proposed the Radius SMOTE technique to improve the performance of the CNN method through the data oversampling process to realize an average accuracy of 82.11% and 78.99% for arousal and valence, respectively [20].…”

Section: -Related Workmentioning

confidence: 99%

“…The process is essential, considering that the signal has low-frequency characteristics and contains many noises [12]. To overcome this problem, a deep learning-based approach using the CNN method was investigated in previous studies [10,30,36,37]. Although, it is unable to characterize the spatial information between channels and frequency bands of the EEG signals.…”

Section: Without Baseline Reduction With Baseline Reductionmentioning

confidence: 99%

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Continuous Capsule Network Method for Improving Electroencephalogram-Based Emotion Recognition

Wirawan

Wardoyo²,

Lelono³

et al. 2022

Emerg Sci J

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The convolution process in the Capsule Network method can result in a loss of spatial data from the Electroencephalogram signal, despite its ability to characterize spatial information from Electroencephalogram signals. Therefore, this study applied the Continuous Capsule Network method to overcome problems associated with emotion recognition based on Electroencephalogram signals using the optimal architecture of the (1) 1st, 2nd, 3rd, and 4th Continuous Convolution layers with values of 64, 128, 256, and 64, respectively, and (2) kernel sizes of 2×2×4, 2×2×64, and 2×2×128 for the 1st, 2nd, and 3rd Continuous Convolution layers, and 1×1×256 for the 4th. Several methods were also used to support the Continuous Capsule Network process, such as the Differential Entropy and 3D Cube methods for the feature extraction and representation processes. These methods were chosen based on their ability to characterize spatial and low-frequency information from Electroencephalogram signals. By testing the DEAP dataset, these proposed methods achieved accuracies of 91.35, 93.67, and 92.82% for the four categories of emotions, two categories of arousal, and valence, respectively. Furthermore, on the DREAMER dataset, these proposed methods achieved accuracies of 94.23, 96.66, and 96.05% for the four categories of emotions, the two categories of arousal, and valence, respectively. Finally, on the AMIGOS dataset, these proposed methods achieved accuracies of 96.20, 97.96, and 97.32% for the four categories of emotions, the two categories of arousal, and valence, respectively. Doi: 10.28991/ESJ-2023-07-01-09 Full Text: PDF

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Section: -Related Workmentioning

confidence: 99%

Section: -Related Workmentioning

confidence: 99%

Section: Without Baseline Reduction With Baseline Reductionmentioning

confidence: 99%

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Continuous Capsule Network Method for Improving Electroencephalogram-Based Emotion Recognition

Wirawan

Wardoyo²,

Lelono³

et al. 2022

Emerg Sci J

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show abstract

“…Examples for EEG-related DSSoCs would be the patient mood detection system proposed by [75] and a sleep staging SoC stated by [76]. PFSoCs are implemented by [77,78].…”

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confidence: 99%

On the Treatment and Diagnosis of Attention Deficit Hyperactivity Disorder with EEG Assistance

Freismuth

TaheriNejad

2022

Electronics

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Attention deficit hyperactivity disorder (ADHD) is a mental disorder most notable in children. The disease may affect the ability to focus and cause a physical and mental restlessness and risky behavior. Recommended treatment consists of stimulant administration and behavioral therapy. However, medicating children is problematic since there are indications that brain development is affected by ADHD medication agents. Therefore, behavioral therapy is the preferred approach in ADHD treatment for children. In order to monitor and optimize the success of such behavioral therapies, neuro-feedback methods can be used. The most notable technology used in such methods is Electroencephalography (EEG). In this article, an overview of the pathology of ADHD, EEG and its usage as a diagnostic and therapeutic tool in the context of ADHD is given. Based on that knowledge, novel EEG measurement modes, new development principles, and system on chip implementations are presented and discussed.

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Practical hyperparameters tuning of convolutional neural networks for EEG emotional features classification

Mezzah¹,

Tari²

2023

Intelligent Systems with Applications

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An Edge AI System-on-Chip Design with Customized Convolutional-Neural-Network Architecture for Real-time EEG-Based Affective Computing System

Cited by 8 publications

References 3 publications

Continuous Capsule Network Method for Improving Electroencephalogram-Based Emotion Recognition

Continuous Capsule Network Method for Improving Electroencephalogram-Based Emotion Recognition

On the Treatment and Diagnosis of Attention Deficit Hyperactivity Disorder with EEG Assistance

Practical hyperparameters tuning of convolutional neural networks for EEG emotional features classification

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