This invention relates to brain-computer interfaces, specifically to a brain-computer interface-based emotion and
stress management system. The
control unit acquires the user's multimodal signals using a non-invasive multimodal
signal acquisition device and preprocesses the EEG signals using an EEG
signal processing module. The
control unit then estimates the power
spectral density of the preprocessed EEG signals using a power
spectral density estimation module to obtain the power
spectral density of the EEG signals. A relaxation level acquisition module obtains the user's relaxation level based on the power spectral density of the EEG signals, a
coefficient of variation acquisition module obtains the
coefficient of variation for the corresponding
frequency band based on the power spectral density of the EEG signals, and a Schumann
resonance energy index acquisition module obtains the Schumann
resonance energy index corresponding to the first and second
harmonics based on the power spectral density of the EEG signals. The technical solution provided by this invention overcomes the shortcomings of existing technologies, such as the difficulty in accurately identifying the user's emotional state and the inability to provide personalized real-time intervention.