Digital input brain-computer interface system based on SEEG signals

Abstract

The invention discloses a digital input brain-computer interface system based on SEEG signals. The digital input brain-computer interface system comprises a stimulation presentation module, a signal acquisition module, a model training module and an online data analysis module. The stimulation presentation module presents a P300 electroencephalogram mode of stimulating and inducing the testee in asingle flickering mode; the signal acquisition module is used for acquiring and amplifying intracranial electroencephalogram signals of a testee; the model training module performs classifier training on the training data to obtain a classifier response model; and the online data analysis module analyzes the online data in real time by utilizing the obtained classifier response model and outputsa result. The system provided by the invention can realize a digital input function by decoding intracranial electrode signals. According to the system, the same or higher input accuracy and information transmission rate can be realized by using fewer electrode channels. Brand-new interactive experience is brought to some epileptic patients, an effective way is provided for clinical scientific research, and potential social value is achieved.

Description

technical field [0001] The invention relates to the technical field of biomedical signal processing technology and brain-computer interface, in particular to a digital input brain-computer interface system based on SEEG (deep intracranial stereotactic electroencephalogram) signals. Background technique [0002] Brain-computer interface technology has become a new type of human-computer interaction in recent years. Users can directly use brain signals to interact with computers without physical movement. Sufferers of other diseases provide a reliable means of interacting with the outside world. At present, most brain-computer interface systems collect non-implantable electrophysiological signals, such as EEG, fMRI, NIRS, etc. However, these signals are restricted in time and space resolution, and these signals are susceptible to external interference, and the signal noise is relatively large, which has a great impact on the performance and efficiency of the system. [0003]...

AI Technical Summary

Problems solved by technology

The brain-computer interface based on the P300 potential has been widely used in EEG, but due to the limitation of the EEG signal itself, there is still a lot of room for improvement in system performance

However, there is still a lack of a lot of effective work on the brain-computer interface based on SEEG signals. Research in this area has greatly improved the brain-computer interface technology.

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