The invention an FSK-SSVEP demodulation system and demodulation algorithm, belonging to the field of brain-computer interface. The system consists of three modules: an SSVEP stimulation generator, anEEG signal acquisition device and a signal processing. After receiving the start signal, the subjects gaze at a specific stimulus; the EEG signal is collected and the command signal is obtained through a demodulation algorithm, and the subjects are prompted to stop gazing at the visual stimulator, thus stopping the data acquisition. The EEG signal acquisition device is used for collecting the SSVEP EEG signal generated by the subject through the fixation stimulation device, and the collected EEG signal is wirelessly transmitted to the software signal processing platform of the intelligent terminal. After receiving the EEG signal, the software processing platform performs preprocessing on the stored signal, and FSK-SSVEP signal demodulation and command recognition, and finally analyzes theeffective components of EEG signals and judges the commands that the subjects are watching.

FSK-SSVEP demodulation system and demodulation algorithm

The present invention discloses a classification method of steady-state visual evoked potential based on empirical mode decomposition. Firstly, collected multichannel steady-state visual evoked potential (SSVEP) is subjected to multi-element empirical mode decomposition to be decomposed into several sub-signals on different frequency band ranges; then according to correlation coefficients betweenthe sub-signals of known classification label signals and template signals, classification suitability indexes corresponding to the sub-signals are calculated; then correlation coefficients between the sub-signals of unknown label signals and the template signals are calculated, the classification suitability indexes are used as a selection weight of sub-signal correlation coefficients, and correlation coefficients between original signals and the template signals are reconstructed; and finally, the steady-state visual evoked potential (SSVEP) is classified according to the reconstruction correlation coefficients between the original signals and the template signals. On a basis of improving a signal-to-noise ratio of the steady-state visual evoked potential (SSVEP), the classification method realizes a relatively high classification accuracy rate for the steady-state visual evoked potential (SSVEP).

Classification method of steady-state visual evoked potential based on empirical mode decomposition

The present invention belongs to the field of brain-computer interface, and for the technical problems that the method in the prior art has high coding difficulty, is difficult to demodulate, and is difficult to adapt to the brain-computer characteristics of users, provides a non-intrusive brain-computer interface coding method of variable-length coding. The method is used for that the human brain sends binary codes which are convenient for the computer to identify to the computer; the human brain generates two first phase signals with a fixed frequency and a fixed stimulation time length, the two first phase signals are respectively corresponding to the binary codes, and a plurality of first phase signals form a symbol; and the interval between the symbols is scaled by changing the time length of the first phase signal in the last bit of the symbol or inserting a coded target. The present invention also discloses a non-intrusive brain-computer interface system of variable-length coding.

Non-intrusive brain-computer interface system and coding method of variable-length coding

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Non-intrusive brain-computer interface system and coding method of variable-length coding

FSK-SSVEP demodulation system and demodulation algorithm