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- 저자 : ( D. Senthil Vadivelan ) (검색결과 3 건)
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( D. Senthil Vadivelan1 ),( Dr. S. Prabhu ),( Dr. M. Uma ) 한국감성과학회 2021 추계학술대회 Vol.2021 No.0
A brain-computer interface (BCI) provides a new means of bridging the gap between humans and computers, now days by allowing computers to be intentionally controlled based on brain signals. The activity of neurons generates electrical impulses, which are recorded by electroencelography (EEG). The acquired EEG signals are used to control the external devices such as a robotic arm, wheelchair, moving cursors, etc., and hence, are very useful to develop personal assistants for the disabled person for interaction and communication to the outside world. This study gives a thorough examination of EEG signal processing in robotic arms control, with a focus on noninvasive brain computer interface systems. For EEG classification, several filtering procedures, feature extraction techniques, machine learning algorithms are explored and summarized.
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( D. Senthil Vadivelan ),( S. Prabhu ),( M. Uma ) 한국감성과학회 2021 한국감성과학회 국제학술대회(ICES) Vol.2021 No.-
A brain-computer interface (BCI) provides a new means of bridging the gap between humans and computers, now days by allowing computers to be intentionally controlled based on brain signals. The activity of neurons generates electrical impulses, which are recorded by electroencelography (EEG). The acquired EEG signals are used to control the external devices such as a robotic arm, wheelchair, moving cursors, etc., and hence, are very useful to develop personal assistants for the disabled person for interaction and communication to the outside world. This study gives a thorough examination of EEG signal processing in robotic arms control, with a focus on noninvasive brain computer interface systems. For EEG classification, several filtering procedures, feature extraction techniques, machine learning algorithms are explored and summarized.
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EEGNet Classification for Enhancing Two-Class EEG-Based Motor Imagery- Brain Computer Interface
( Senthil Vadivelan. D ),( Prabhu Sethuramalingam ) 한국감성과학회 2023 한국감성과학회 국제학술대회(ICES) Vol.2023 No.-
Effective signal classification of motor imagery (MI) plays a pivotal role in the development of brain-computer interfaces (BCI). Paradigms of braincomputer interfaces (BCI) empower individuals to establish communication with the external world exclusively through their brain's intentions. While convolutional neural networks have seen a gradual adoption in the task of classifying motor imagery (MI) and have achieved impressive performance, several challenges persist, making the effective decoding of raw EEG signals a demanding task. These challenges include: 1) non-linearity, non-stationarity, and low signal-to-noise ratio inherent in EEG signals. 2) Many existing end-to-end MI models employ a single-scale convolution, which constrains the classification results as the optimal convolution scale varies among different subjects, a phenomenon known as subject variability. In this study, we address the aforementioned challenges by employing EEGnet to classify the lefthand and righthand motor imagery movements, a highly efficient and streamlined deep learning framework. The methodology presented in this study is assessed using MI datasets from BCI Competition IV 2a, achieving classification accuracies of 89%. These classification outcomes establish the proposed methodology as an effective approach for future BCI system design.
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