An EEG dataset for studying asynchronous steady-state visual evoked potential (SSVEP) based brain computer interfaces
Compared with the commonly used synchronous brain-computer interface (BCI), the asynchronous BCI is a more flexible and natural way to control the real-world robotic devices. The major difficulty of building a robust asynchronous BCI lies in the discrimination between control states (CSs) and non-co...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Brain-Apparatus Communication |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/27706710.2024.2418650 |
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| author | Jing Zhao Qian Zhang Xinrui Wang Xueshuo Liu Jiaxin Li Fengjie Fan Zhenhu Liang Xiaoli Li |
| author_facet | Jing Zhao Qian Zhang Xinrui Wang Xueshuo Liu Jiaxin Li Fengjie Fan Zhenhu Liang Xiaoli Li |
| author_sort | Jing Zhao |
| collection | DOAJ |
| description | Compared with the commonly used synchronous brain-computer interface (BCI), the asynchronous BCI is a more flexible and natural way to control the real-world robotic devices. The major difficulty of building a robust asynchronous BCI lies in the discrimination between control states (CSs) and non-control states (NSs). This article presents an open-source 63-channel electroencephalogram (EEG) dataset of 24 subjects for asynchronous steady-state visual evoked potential (SSVEP)-BCI research. The data was recorded from an SSVEP based CS task and three different types of NS tasks, namely NS1, NS2 and NS3. The dataset was evaluated using three processes, including analysis of temporal waveform, amplitude spectrum and signal-to-noise (SNR), recognition of SSVEP frequencies, and classification between CS and NS. The dataset can be used to support studies of asynchronous classification, NS detection, and discrimination between different NSs. |
| format | Article |
| id | doaj-art-67f43e94d1764aecaf151eafdfa6a70a |
| institution | OA Journals |
| issn | 2770-6710 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Brain-Apparatus Communication |
| spelling | doaj-art-67f43e94d1764aecaf151eafdfa6a70a2025-08-20T01:57:54ZengTaylor & Francis GroupBrain-Apparatus Communication2770-67102024-12-013110.1080/27706710.2024.2418650An EEG dataset for studying asynchronous steady-state visual evoked potential (SSVEP) based brain computer interfacesJing Zhao0Qian Zhang1Xinrui Wang2Xueshuo Liu3Jiaxin Li4Fengjie Fan5Zhenhu Liang6Xiaoli Li7Department of Electrical Engineering, Yanshan University, Qinhuangdao, ChinaDepartment of Electrical Engineering, Yanshan University, Qinhuangdao, ChinaSuzhou Automobile Research Institute, Tsinghua University, Suzhou, ChinaDepartment of Electrical Engineering, Yanshan University, Qinhuangdao, ChinaDepartment of Electrical Engineering, Yanshan University, Qinhuangdao, ChinaDepartment of Electrical Engineering, Yanshan University, Qinhuangdao, ChinaDepartment of Electrical Engineering, Yanshan University, Qinhuangdao, ChinaGuangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Guangzhou, ChinaCompared with the commonly used synchronous brain-computer interface (BCI), the asynchronous BCI is a more flexible and natural way to control the real-world robotic devices. The major difficulty of building a robust asynchronous BCI lies in the discrimination between control states (CSs) and non-control states (NSs). This article presents an open-source 63-channel electroencephalogram (EEG) dataset of 24 subjects for asynchronous steady-state visual evoked potential (SSVEP)-BCI research. The data was recorded from an SSVEP based CS task and three different types of NS tasks, namely NS1, NS2 and NS3. The dataset was evaluated using three processes, including analysis of temporal waveform, amplitude spectrum and signal-to-noise (SNR), recognition of SSVEP frequencies, and classification between CS and NS. The dataset can be used to support studies of asynchronous classification, NS detection, and discrimination between different NSs.https://www.tandfonline.com/doi/10.1080/27706710.2024.2418650Brain–computer interface (BCI)electroencephalogram (EEG)steady-state visual evoked potential (SSVEP)asynchronousnon-control state (NS) |
| spellingShingle | Jing Zhao Qian Zhang Xinrui Wang Xueshuo Liu Jiaxin Li Fengjie Fan Zhenhu Liang Xiaoli Li An EEG dataset for studying asynchronous steady-state visual evoked potential (SSVEP) based brain computer interfaces Brain-Apparatus Communication Brain–computer interface (BCI) electroencephalogram (EEG) steady-state visual evoked potential (SSVEP) asynchronous non-control state (NS) |
| title | An EEG dataset for studying asynchronous steady-state visual evoked potential (SSVEP) based brain computer interfaces |
| title_full | An EEG dataset for studying asynchronous steady-state visual evoked potential (SSVEP) based brain computer interfaces |
| title_fullStr | An EEG dataset for studying asynchronous steady-state visual evoked potential (SSVEP) based brain computer interfaces |
| title_full_unstemmed | An EEG dataset for studying asynchronous steady-state visual evoked potential (SSVEP) based brain computer interfaces |
| title_short | An EEG dataset for studying asynchronous steady-state visual evoked potential (SSVEP) based brain computer interfaces |
| title_sort | eeg dataset for studying asynchronous steady state visual evoked potential ssvep based brain computer interfaces |
| topic | Brain–computer interface (BCI) electroencephalogram (EEG) steady-state visual evoked potential (SSVEP) asynchronous non-control state (NS) |
| url | https://www.tandfonline.com/doi/10.1080/27706710.2024.2418650 |
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