A Real-Time Wireless Brain–Computer Interface System for Drowsiness Detection

Abstract

A real-time wireless electroencephalogram (EEG)-based brain-computer interface (BCI) system for drowsiness detection has been proposed. Drowsy driving has been implicated as a causal factor in many accidents. Therefore, real-time drowsiness monitoring can prevent traffic accidents effectively. However, current BCI systems are usually large and have to transmit an EEG signal to a back-end personal computer to process the EEG signal. In this study, a novel BCI system was developed to monitor the human cognitive state and provide biofeedback to the driver when drowsy state occurs. The proposed system consists of a wireless physiological signal-acquisition module and an embedded signal-processing module. Here, the physiological signal-acquisition module and embedded signal-processing module were designed for long-term EEG monitoring and real-time drowsiness detection, respectively. The advantages of low owner consumption and small volume of the proposed system are suitable for car applications. Moreover, a real-time drowsiness detection algorithm was also developed and implemented in this system. The experiment results demonstrated the feasibility of our proposed BCI system in a practical driving application.

EEG Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers

“…The small dotted box in Fig. 3 describes the MP classifier, comprising two modules, where the first module is an IT2FS neural net with outputs C 1 , C 2 , C 3 and C 4 . This neural net is realized with IT2FS neurons, the symbol and architecture of which are given but k C =0 for any [1,3].…”

“…Among the well-known brain signal acquisition techniques, electroencephalography (EEG) [1] is most popular for its prompt time-response [2], non-invasive characteristic [3], [4] portability and cost-effectiveness. Because of the above merits, the paper attempts to employ EEG-signal processing and classification to detect VA failure (VAF), MP failure (MPF) and ME failure (MEF).…”

Filter Band Multicarrier Based Transmission Technology for Clinical EEG Signals

“…In addition, in their study, personality traits, sensory input, neuronal activity, conductive tissues, electrode-tissue interface, miniaturized and ergonomic EEG headsets, wireless and wearable EEG system designs, brain activity analysis, and output interfaces were discussed. Lin et al [2] proposed a Bluetooth-based real-time brain-computer interface (BCI) system that can be used to detect drowsiness while driving. This system had integrated wireless physiological signal-acquisition and embedded signal-processing modules, and it featured real-time wireless drowsiness detection, long-term daily life EEG monitoring, high computation capacity, and low power consumption.…”

Drowsiness detection for single channel EEG by DWT best m-term approximation

“…Previous studies commonly used Fast Fourier Transform (FFT) or Discrete Wavelet Transform (DWT) (Akay, 1998;Correa and Leber, 2010;De Carli et al, 1999;Liang et al, 2006;Subasi, 2005) as a feature selection technique, while Mahalanobis distance (Lin et al, 2010), independent component analysis (Lin et al, 2005) or neural network were used as classifiers (Subasi, 2005). Correa and Leber (2010) also used an artificial neural network to classify drowsiness through the extracted characteristics from an EEG signal using wavelets and Fourier spectrum.…”

“…The technique can be attractive for implementing fatigue detection on low-power devices because DWT kernels require fewer execution steps than FFT algorithms (complexities for transforming 1D signals with N points are respectively in the order of N and N.log 2 (N) steps (Beylkin et al, 1991)). Additionally, simple threshold techniques are employed instead of classification strategies based on heavy matrix operations (Lawhern et al, 2013;Liang et al, 2006;Lin et al, 2010). Another advantage of the proposed technique is the fact that no previous artifact rejection is necessary, since the thresholded wavelet representation of the EEG signal already works as a type of filtering process, removing low frequencies from the analyzed signal.…”