5.2 Driving Performance and Unsupervised Analysis
5.2.5 DSP Module Programming
The flowchart of DSP module was shown in Fig. 5-10. In program development, we used multithread to build up a real-time analysis system, moreover to increase program’s flexibility and the use of performance.
Each thread is independent. In the DSP module’s main loop, we just create the threads we want and joint them. The system kernel will automatically schedule those threads and decrease the system waiting cost. In thread 1, Real-time detect EEG raw data from Blue Tooth, and go on pass through a moving average to cut-off at 32Hz, further down sample to 64 point in 1 second. Thread 2 handles FFT process. First, the FFT result will be transmit into 3 minute array in alert model. When array is full, the theta and alpha’s mean vector and covariance matrix in thread 3. Thread 4 mainly handles the MDT and MDA converter, then based on above optimal conclusion to calculate the MDC (a=0.9). If the values of MDC are higher than threshold in 7.5, the thread 5 will be switch on and make some warning voice in thread 5.
On the other hands, the program’s user interface could directly tell user how was his / her physiological conditions. Further, let users easy handle this system. The user interface’s flowchart was shown in Fig. 5-11. Following this flowchart, when the boot loader setup, the real-time drowsy detection program will be automatically started by DSP module. If user finished dress the portable EEG acquisition module over, he / she push the start button to start to detect real-time EEG raw data. Then the screen could print the real-time data. Furthermore, according to the mean vector and covariance matrix of alert model, the linear combination of MDT and MDA was counted continually, and the result value will also print on the screen’s bottom side. Following Fig. 5-12 showed, the screen’s update time we set was changed in every 1 second, so we could show total 1 seconds EEG raw data and result of MDC at the same time on the TFT-LCD, and the expanded SD card circuit will detect a new SPI command from DSP module to ring the buzzer or not in every 1 second. By the way, user could push the quit button to end this program.
Fig. 5-11: The user interface’s flowchart
Fig. 5-12: The block diagram of dataflow
Chapter6 Conclusions
In this study, a real-time wireless brain computer interface for drowsiness detection was proposed. Here, a portable wireless EEG acquisition module and a DSP module were developed. The portable wireless EEG acquisition module was designed to acquire EEG signal, and then transmit them into the DSP module wirelessly to detect drowsiness. The modular approach applied in hardware and software design enables this system to be configurable for different application scenarios. For example, in the future, the EEG acquisition module can be used to connect several optional physiological sensors in addition to the built-in one, and it doesn’t affect the whole system architecture. This system is feasible for further extension. Moreover, our EEG acquisition module is small, light, and wearable, therefore, it is suitable for long-term EEG monitoring in users’ daily life.
A novel algorithm based on [59] for drowsiness detection was also proposed in this study. It can effectively reduce computation complexity, and is suitable to be implemented in the DSP module, and it is good at removing the differences between individual and environment in different people or measurements. Some previous studies indicated that the level of drowsiness is proportional with the increase of alpha and theta rhythms in EEG. Under the assumption of that driving trajectory is proportional with the level of drowsiness, our experimental results showed that the power of alpha and theta rhythms (the average MDT and MDA) in EEG increased indeed when the level of drowsiness increased, and the linear combination of alpha and theta rhythms (MDC) with factor a = 0.9 had the highest correlation (0.6271) with the level of drowsiness.
In this study, the levels of drowsiness were defined as follows: alertness (0.2 - 1s),
slight drowsiness (1 - 2s), extreme drowsiness (2 - 3s), and sleepiness (over 3s). In order to verify the reliability of our proposed algorithm, we simplified four cognitive states into two: alert state and drowsy state (combining slight, deep and extreme drowsiness), and then the binary classification test was used to investigate the sensitivity and positive predictive value of our algorithm with different thresholds.
Our experimental results showed that MDC with factor a = 0.9 when threshold was set to 7.5 had the highest F-measure value (F-measure = 77.59%, sensitivity = 88.28%, and positive predictive value = 69.21%). However, the accurate of our algorithm for drowsiness detection seems not good enough. This can explained by that each increase of alpha and theta rhythm may not correspond to each drowsy event although the long-term increasing trend of power of alpha and theta rhythm is proportional with the level of drowsiness. In future work, our system could combine with the utility of other physiological parameters, such as EOG and EMG, to improve both the sensitivity and positive predictive value.
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