Abstract: The present disclosure discloses an apparatus for a brain computer interface (BCI) including a feature extraction filter trainer for training a feature extraction filter which minimizes an influence of a background brain wave while maximizing a difference between intended brain waves; and a classifier trainer for training a classifier for classifying the intended brain waves by using a feature vector obtained by filtering the intended brain wave at the feature extraction filter. With the apparatus, only the background brain wave is additionally measured, such that previous intended brain wave data can be reused and the brain wave can be classified more quickly and accurately.

Abstract: An apparatus and a method is provided for detecting biometric signals of a driver and classifying the driver into a normal state or a fatigued state based on the biometric signals. An apparatus may include: a biometric signal measuring part configured to measure the biometric signals including a blood flow rate of a brain of the driver using an electro-encephalography (EEG), an electro-cardiography (ECG), and a functional near-infrared spectroscopy (fNIRS) of the driver; a biometric signal integral part configured to integrate the measured biometric signals, to extract characteristics of the respective biometric signals from the measured biometric signals and to then integrate the extracted characteristics, or to classify the extracted characteristics of the biometric signals and to then integrate the classified characteristics; and a driver state detecting part configured to detect the state of the driver based on the integrated biometric signals.

Abstract: An apparatus and a method is provided for detecting biometric signals of a driver and classifying the driver into a normal state or a fatigued state based on the biometric signals. An apparatus may include: a biometric signal measuring part configured to measure the biometric signals including a blood flow rate of a brain of the driver using an electro-encephalography (EEG), an electro-cardiography (ECG), and a functional near-infrared spectroscopy (fNIRS) of the driver; a biometric signal integral part configured to integrate the measured biometric signals, to extract characteristics of the respective biometric signals from the measured biometric signals and to then integrate the extracted characteristics, or to classify the extracted characteristics of the biometric signals and to then integrate the classified characteristics; and a driver state detecting part configured to detect the state of the driver based on the integrated biometric signals.

Abstract: The present disclosure discloses an apparatus for a brain computer interface (BCI) including a feature extraction filter trainer for training a feature extraction filter which minimizes an influence of a background brain wave while maximizing a difference between intended brain waves; and a classifier trainer for training a classifier for classifying the intended brain waves by using a feature vector obtained by filtering the intended brain wave at the feature extraction filter. With the apparatus, only the background brain wave is additionally measured, such that previous intended brain wave data can be reused and the brain wave can be classified more quickly and accurately.