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Review a BCI research work that was nominated for the BCI award, and create a short video to present your digest of this work.
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Paper：Regulation of arousal via online neurofeedback improves human performance in a demanding sensory-motor task
- Deal with EEG data preprocessing
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Utilize Convolutional Neural Network (CNN) for motor imagery(MI) EEG classification tasks, with experiments on 4 training schemes and 3 model architectures.
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I use conv3D to solve the SCCNet_v2 bonus problem.
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Select a paper from the candidate list and reproduce the results including data preprocessing on the provided unprocessed BCIC-IV 2a dataset, implementing the proposed model in the paper, and finally compare its performance with the 3 baseline models in HW3.
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Paper：FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface Motor imagery-BCI
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For the final car competition, you will be extending your work from the midterm mind-controlled car demo by controlling the car to turn right and left using hybrid-BCI approach.
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We use this github to record and analysis our data ：https://github.com/HeosSacer/SSVEP-Brain-Computer-Interface
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Use alpha wave and utils.py to control our car.
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peak_alpha = the sum of frequency in alpha wave(8-13Hz)/the sum of the frequency in 3-30Hz
peak_alpha = sum of frequency in alpha wave(8-13Hz)/ sum of the frequency in 3-30Hz
if (peak_alpha > threshold):
action point to 1
else:
action point to 0- Using 2-bit to control the car : forwad, left, right. ex. 00, 10, 11, 10