A Study on the Content of Mental and Physical Stability Game in Virtual Reality through EEG Detection

Lee, Jongwon; Lee, Dayeon; Jeong, Incheol

doi:10.1109/ictc52510.2021.9620932

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…A BCI system decodes the patient’s intentions to move specific limbs, and subsequently uses these decoded intentions to provide corresponding sensorimotor feedback to the patient in various forms ( Mane et al, 2020 ). In addition to being widely employed in the field of medical rehabilitation, BCI offers significant room for growth in fields like sleep monitoring, brain disease detection, and game entertainment ( Chen et al, 2018 ; Arpaia et al, 2020 ; Lee et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

MSATNet: multi-scale adaptive transformer network for motor imagery classification

Hong

Liu

2023

Front. Neurosci.

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Motor imagery brain-computer interface (MI-BCI) can parse user motor imagery to achieve wheelchair control or motion control for smart prostheses. However, problems of poor feature extraction and low cross-subject performance exist in the model for motor imagery classification tasks. To address these problems, we propose a multi-scale adaptive transformer network (MSATNet) for motor imagery classification. Therein, we design a multi-scale feature extraction (MSFE) module to extract multi-band highly-discriminative features. Through the adaptive temporal transformer (ATT) module, the temporal decoder and multi-head attention unit are used to adaptively extract temporal dependencies. Efficient transfer learning is achieved by fine-tuning target subject data through the subject adapter (SA) module. Within-subject and cross-subject experiments are performed to evaluate the classification performance of the model on the BCI Competition IV 2a and 2b datasets. The MSATNet outperforms benchmark models in classification performance, reaching 81.75 and 89.34% accuracies for the within-subject experiments and 81.33 and 86.23% accuracies for the cross-subject experiments. The experimental results demonstrate that the proposed method can help build a more accurate MI-BCI system.

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Section: Introductionmentioning

confidence: 99%

MSATNet: multi-scale adaptive transformer network for motor imagery classification

Hong

Liu

2023

Front. Neurosci.

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“…which is promising for a wide range of applications, such as rehabilitation, smart house systems, entertainments, and so forth [1], [2], [3], [4], [5], [6]. In general, a complete and robust EEG-based BCI system requires a calibration phase and a testing phase [7].…”

mentioning

confidence: 99%

Transfer Learning With Optimal Transportation and Frequency Mixup for EEG-Based Motor Imagery Recognition

Chen

Wang

Sun

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Electroencephalography-based Brain Computer Interfaces (BCIs) invariably have a degenerate performance due to the considerable individual variability.To address this problem, we develop a novel domain adaptation method with optimal transport and frequency mixup for cross-subject transfer learning in motor imagery BCIs. Specifically, the preprocessed EEG signals from source and target domain are mapped into latent space with an embedding module, where the representation distributions and label distributions across domains have a large discrepancy. We assume that there exists a nonlinear coupling matrix between both domains, which can be utilized to estimate the distance of joint distributions for different domains. Depending on the optimal transport, the Wasserstein distance between source and target domains is minimized, yielding the alignment of joint distributions. Moreover, a new mixup strategy is also introduced to generalize the model, where the inputs trials are mixed in frequency domain rather than in raw space. The extensive experiments on three evaluation benchmarks are conducted to validate the proposed framework. All the results demonstrate that our method achieves a superior performance than previous state-of-the-art domain adaptation approaches.

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Game-Stress-Tracker: EEG-Based Smart Advisor Bot for Stress Detection During Playing BGMI Game

Das,

Malick,

Dey

et al. 2023

Lecture Notes in Electrical Engineering

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A Study on the Content of Mental and Physical Stability Game in Virtual Reality through EEG Detection

Cited by 5 publications

References 7 publications

MSATNet: multi-scale adaptive transformer network for motor imagery classification

MSATNet: multi-scale adaptive transformer network for motor imagery classification

Transfer Learning With Optimal Transportation and Frequency Mixup for EEG-Based Motor Imagery Recognition

Game-Stress-Tracker: EEG-Based Smart Advisor Bot for Stress Detection During Playing BGMI Game

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