JNEEG shield for Jetson Nano for real-time EEG signal processing with deep learning
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Accesso online: | |
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| _version_ | 1866917667622879232 |
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| author | Rakhmatulin, Ildar |
| author_facet | Rakhmatulin, Ildar |
| contents | The article presents an accessible route into the field of neuroscience through the JNEEG device. This device allows converting the Jetson Nano board into a brain-computer interface, making it easy to measure EEG, EMG, and ECG signals with 8 channels. With Jetson Nano is possible use deep learning for real-time signal processing and feature extraction from EEG in real-time without any data transmission. Over the past decade, the proliferation of artificial intelligence has significantly impacted various industries, including neurobiology. The integration of machine learning techniques has opened avenues for practical applications of EEG signals across technology sectors. This surge in interest has led to the widespread popularity of low-cost brain-computer interface devices capable of recording EEG signals using non-invasive electrodes. JNEEG device demonstrates satisfactory noise levels and accuracy for use in applied tasks with machine learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_09575 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | JNEEG shield for Jetson Nano for real-time EEG signal processing with deep learning Rakhmatulin, Ildar Signal Processing The article presents an accessible route into the field of neuroscience through the JNEEG device. This device allows converting the Jetson Nano board into a brain-computer interface, making it easy to measure EEG, EMG, and ECG signals with 8 channels. With Jetson Nano is possible use deep learning for real-time signal processing and feature extraction from EEG in real-time without any data transmission. Over the past decade, the proliferation of artificial intelligence has significantly impacted various industries, including neurobiology. The integration of machine learning techniques has opened avenues for practical applications of EEG signals across technology sectors. This surge in interest has led to the widespread popularity of low-cost brain-computer interface devices capable of recording EEG signals using non-invasive electrodes. JNEEG device demonstrates satisfactory noise levels and accuracy for use in applied tasks with machine learning. |
| title | JNEEG shield for Jetson Nano for real-time EEG signal processing with deep learning |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2405.09575 |