WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that its R&D team had designed a CNN-based P300 BCI game model and proposed a Bayesian deep learning-based algorithm that solves the overfitting problem when training on small data sets. The successful application of P300 to the game model species proves that it can be applied to deep learning algorithms for online BCI systems.
The system framework of this game model contains three subsystems, namely, a data acquisition part, a data processing part, and a vision and game terminal. In the data acquisition part, multi-channel scalp EEG signals are recorded using electrode caps and amplifiers. After the signals are pre-processed, the data processing part can be divided into two steps: offline training and online classification testing. Finally, the classification results are converted into operation commands and sent to the vision and game terminals. The visual and game terminal consists of two sub-steps: (1) providing visual stimuli to the user after the stimulus strategy update and (2) providing visual feedback (output coordinates) to the user.
EEG data acquisition and pre-processing
WiMi's BCI game model uses a 32-channel capacitor and amplifier to record EEG data non-invasively by digitizing at 1000 Hz and filtering using a 50 Hz trap filter. The system collects all electrode data. The recorded data were first filtered to reduce the effect of filtering edge effects. Band-pass filtering is used for the EEG signal from each channel, and the system captures the necessary information for the P300 signal after stimulation. Then, the system downsamples the data. A data matrix of identical characters is superimposed and averaged to reduce the signal-to-noise ratio.