2.1 Brain Computer Interface
Brain computer interface (BCI) is an interface between human and computer. It is based on the specific brain activity generated by a specific thought of a human.
That is, we can obtain information from brain activity via signal processing and use the recognized pattern to control a computer. At the beginning, the purpose of BCI is not only prosthesis but also is to help handicapped people [48], gradually. Because of the disability of muscle, handicapped people can not do things independently. For example, handicapped people cannot move, control devices without aid. Hence, to help these handicapped people, many researchers have devoted themselves to develop BCI. That is, as long as handicapped people are still cognitively healthy, they might able to move on an automatic wheel chair, and control the on/off switches of lamps via EEG recording and analysis. Through decades, it have been found in many studies that the cognitive state of a person can be extracted from brain activity [1][2]. More and more researchers are devoted to the study of BCI. BCI has helped handicapped to live independent. Recent studies in primates, human subjects of Serruya et al. and Taylor et al. [3][4] have demonstrated that animals can learn to utilize their brain activity to control the displacements of computer cursors. Chapin et al. and Wessberg et al. also demonstrated that animals can learn to utilize their brain activity to control one- (1D) to three-dimensional (3D) movements of simple and elaborate robot arms [5][6]. However, many domestic researches were focusing on EEG data recording instead of EEG analysis [7]-[13]. Gao et al. have developed wireless BCI based on steady-state visual evoked potential (SSVEP) [14]. They used twelve buttons illuminated at different rates on a computer monitor to simulate a telephone. Users could input phone numbers by gazing at these buttons. The frequency-coded SSVEP
was used to judge which button the user attended to. Another study of Gao et al. used digital signal processor (DSP) to process EEG signals and wirelessly controlled appliances with visual evoked stimulus [15]. Pfurtscheller et al. have designed and implemented an EEG-based communication device called “Virtual Keyboard” (VK).
Classification of the EEG patterns was based on band power estimates and hidden Markov models (HMMs) [16][17]. Another research of Pfurtscheller et al. proposed an EEG-based Pocket BCI system that converted brain activity into control signals left and right direction of a wheelchair [18]. Ashwin et al. described [19] a system that monitored EEG of epileptic patients to improve the quality of their lives and also helped healthcare providers to make a better diagnosis for patients with neurological disorders. The use of Bluetooth connectivity helps physicians to monitor patient activity while the patient resumes his or her normal activity.
Compared to the portability, many other applications using ECG are always based on embedded systems. The EEG-based application on embedded systems is rarely seen. Han-Nam et al. have developed a system which enabled a patient to be treated at home through digital telemetry and public communication line. ECG signals were transmitted wirelessly and not hindering a patient's movement [20]. Other ECG systems only record ECG signals and leave the signal analysis to doctors [21][22].
Traditionally, a BCI system can be divided into two functional blocks. One is data acquisition and recognition part, another is control part. An example of BCI is shown as Fig.2-1. Jose. et al. demonstrate that primates can learn to reach and grasp virtual objects by controlling a robot arm through a closed-loop brain–machine interface (BMIc) [30]. It uses multiple mathematical models to extract several motor parameters (i.e., hand position, velocity, gripping force, and the EMGs of multiple arm muscles) from the electrical activity of frontal parietal neuronal ensembles. Using visual feedback, monkeys succeeded in producing robot reach-and-grasp movements
even when their arms did not move.
Fig.2- 1 Learning to Control a BMIc for Reaching and Grasping by Primates
2.2 Independent Component Analysis
The original ICA was used for voice signal separation. A classical application of ICA is the “cocktail party problem”, where a number of people are talking simultaneously in a room (like at a cocktail party), and one is trying to follow one of the discussions. The human brain can handle this sort of auditory source separation problem, but it is a very difficult problem in digital signal processing.
EEG is a non-invasive record of brain electrical activity measured as changes in potential difference between pairs of electrodes placed on the human scalp. Because of volume conduction through brain tissue, cerebrospinal fluid, skull, and scalp, EEG data collected anywhere on the scalp mixes signals from multiple suitably-oriented cortical areas. Thus, the analysis of EEG signals is also a “cocktail party problem”
and often very challenging. One of the most pervasive problems in EEG analysis and interpretation is the interference in the data produced by often large and distracting artifacts arising from eye movements, eye blinks, muscle noise, heart signals, and line noise. One common strategy for avoiding EEG artifacts is to reject all EEG recordings containing artifacts larger than some arbitrarily selected EEG voltage value.
However, when limited data are available, or when blinks and muscle movements occur too frequently as in children and some patient groups, the amount of data lost to artifact rejection may be unacceptable. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multi-channel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Jung et al. [23][24] use ICA for removing a wide variety of artifacts from EEG records. Their results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artificial sources in EEG records with results comparing favorably with those obtained using regression and PCA methods.
Nevertheless, most ICA was done off-line on PCs, hindering portability and on-line BCI. This study reports an implementation of an ICA algorithm, making on-line blind source separation of the multi-channel EEG a reality.
2.3 Drowsiness Detection Methods
With the increasing causalities on highway due to drowsiness, many researchers have devoted to develop algorithms to prevent drowsiness. One method is to detect drowsiness based on changes in blink behavior. The drowsiness detection Ulrika proposed was based on changes in blink behavior and classification was made on a four graded scale using MATLAB [25]. Although several studies showed that eye-activity variations were highly correlated with the human fatigue and can accurately and quantitatively estimate alertness levels, the step size (temporal resolution) of those eye-activity based methods is often too slow (10s or longer) to track fast changes in vigilance. Another method is to detect drowsiness information by heart rate monitoring sensors placed in the steering wheel [26]. But the drowsiness
classification needs coordination, its not autonomy parameter.. Matsushita et al. have developed a wearable fatigue monitoring system with a 2-axis accelerometer and an on-board signal processing microcontroller [27]. As a result, the measured values of the acceleration trace length showed some inconsistency with user-interviews consist of subjective questionnaires about the user's fatigue [27].
It has also been known for more than half a century that signal changes related to alertness, arousal, sleep, and cognition are present in EEG signals, but relatively little has been done to capture this information in real time [25]. Recently, Lin et al.
have developed a drowsiness-estimation system based on EEG by combining ICA, power-spectrum analysis, correlation evaluations, and linear regression model to estimate a driver’s cognitive state when he/she drove a car in a virtual reality (VR)-based dynamic simulator [28][29]. The proposed ICA-based method applied to power spectrum of ICA components successfully removed most of EEG artifacts and estimated the driver’s drowsiness fluctuation indexed by the driving performance measure. However, their drowsiness detection was still performed off-line on a personal computer. For eventual practical acceptance in the workplace, it is highly desirable to make all data acquisition and analysis on-lined. In this study, we design, develop and demonstrate an embedded wireless BCI that comprises three functional modules: EEG recordings, amplification, digitization and wireless transmission, on-line ICA process and spectral estimation, and real-time drowsiness detection algorithm to accurately and continuously detect subject drowsiness level based on the EEG data in near real time.