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Performance comparison using m-sequence stimulation for cVEP BCI.

Figshare2018-10-22 更新2026-04-29 收录

数据链接：

https://figshare.com/articles/dataset/Performance_comparison_using_m-sequence_stimulation_for_cVEP_BCI_/7237145 数据链接链接失效反馈

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资源简介：

Performance comparison using m-sequence stimulation for cVEP BCI.

应用场景：

创建时间：

2018-10-22

视觉诱发电位脑机接口

可穿戴设备评估

The brain-computer interfaces (BCIs) provide humans a new communication channel by encoding and decoding brain activities. Steady-state visual evoked potential (SSVEP)-based BCI stands out among many

NIAID Data Ecosystem30

Brain response to luminance-based and motion-based stimulation using inter-modulation frequencies

视觉诱发电位脑机接口

视觉刺激调制

Steady state visual evoked potential (SSVEP)-based brain computer interface (BCI) has advantages of high information transfer rate (ITR), less electrodes and little training. So it has been widely inv

NIAID Data Ecosystem20

Training accuracy result of the BHHSHO-DL method under the BCIC-IV database.

脑机接口

脑电信号解码

Training accuracy result of the BHHSHO-DL method under the BCIC-IV database.

Figshare2024-11-21 更新10

Reconstruction of Bangla Digits from Brain Signal: A Neural Decoding Approach.

脑电信号解码

数字识别重建

This work has been done as a undergrade thesis work. Some of the EEG data related to this work are shared here.

NIAID Data Ecosystem20

EEGDash/nm000143

脑机接口运动想象

脑电信号解码

BNCI2003_IVa Motor Imagery数据集是一个包含脑电图（EEG）信号的数据集，主要用于视觉运动想象任务的研究。数据集包含5名健康受试者的5次记录，每次记录使用118个通道，采样率为100 Hz。数据集的总时长为4小时，大小为492.7 MB。数据集的许可证为CC-BY-4.0，适用于脑机接口（BCI）和神经科学研究。数据集可以通过EEGDash工具加载，支持PyTorch和br

Hugging Face2026-04-21 更新20

Performance comparison using m-sequence stimulation for cVEP BCI.

资源简介：

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