The present study used the experimental patterns of Go/No Go and no motion contingent negative variation (CNV) task into the research in order to study whether the CNV can express the implication of expectation. Through comparing the CNV under different conditions, the data collected from experiment showed that the key to evoked CNV was close to the warning signal and command signal. Whether the command signal was related to the task would impact on the amplitude of the CNV. This characteristics responses to the subjects' expectation. On this basis, CNV can be used as the electrophysiological index for the reflection of expected value in the conditions of this experiment.
Feature extraction is a very crucial step in P300-based brain-computer interface (BCI) and independent component analysis (ICA) is a suitable P300 feature extraction method. But at present the convergence performance of the general ICA iteration methods are not very satisfactory. In this paper, a method based on quantum particle swarm optimizer (QPSO) algorithm and ICA technique is put forward for P300 extraction. In this method, quantum computing is used to impel ICA iteration to globally converge faster. It achieved the purpose of extracting P300 rapidly and efficiently. The method was tested on two public datasets of BCI Competition Ⅱ and Ⅲ, and a simple linear classifier was employed to classify the extracted P300 features. The recognition accuracy reached 94.4% with 15 times averaged. The results showed that the proposed method could extract P300 rapidly and the extraction effect did not reduce. It provides an experimental basis for further study of real-time BCI system.
Using electroencephalogram (EEG) signal to control external devices has always been the research focus in the field of brain-computer interface (BCI). This is especially significant for those disabilities who have lost capacity of movements. In this paper, the P300-based BCI and the microcontroller-based wireless radio frequency (RF) technology are utilized to design a smart home control system, which can be used to control household appliances, lighting system, and security devices directly. Experiment results showed that the system was simple, reliable and easy to be populirised.
The electroencephalographic characteristics of mental fatigue, which was induced by long-term working memory task of 2-back, were studied by event-related potential (ERP) technology in order to obtain objective evaluation indicators for mental fatigue. Thirty-two healthy male subjects, 22–28 years old, were divided into two groups evenly, one is un-fatigue group and the other is fatigue group. The fatigue group performed a 2-back task for 100 min continuously, while the un-fatigue group just performed a 2-back task at the first and last 10 min respectively, and rested during the middle 80 min. The subjective levels of fatigue, task performance and electroencephalogram were recorded. The impaired thought and attention states, enhanced sleepy and fatigue feeling were found in the fatigue group, meanwhile their reaction time to 2-back task extended, and the accuracy decreased significantly. These results verified the validity of mental fatigue model induced by 2-back task, and then the ERP characteristic parameters were compared and analyzed between fatigue group and un-fatigue group. The results showed that the fatigue group’s amplitudes of P300 (F = 2.539, P < 0.05) and error-related negativity (ERN) ( F = 10.040, P < 0.05) decreased significantly along with the increase of fatigue comparing with the un-fatigue group, however, there were no significant change in other parameters (all P > 0.05). These results demonstrate that P300 and ERN can be considered as potential evaluation indictors for mental fatigue induced by long-term working memory task, which will provide basis for the future exploring of countermeasure for mental fatigue.
Individual differences of P300 potentials lead to that a large amount of training data must be collected to construct pattern recognition models in P300-based brain-computer interface system, which may cause subjects’ fatigue and degrade the system performance. TrAdaBoost is a method that transfers the knowledge from source area to target area, which improves learning effect in the target area. Our research purposed a TrAdaBoost-based linear discriminant analysis and a TrAdaBoost-based support vector machine to recognize the P300 potentials across multiple subjects. This method first trains two kinds of classifiers separately by using the data deriving from a small amount of data from same subject and a large amount of data from different subjects. Then it combines all the classifiers with different weights. Compared with traditional training methods that use only a small amount of data from same subject or mixed different subjects’ data to directly train, our algorithm improved the accuracies by 19.56% and 22.25% respectively, and improved the information transfer rate of 14.69 bits/min and 15.76 bits/min respectively. The results indicate that the TrAdaBoost-based method has the potential to enhance the generalization ability of brain-computer interface on the individual differences.
Increasing the amplitude of event-related potential is one of the key methods to improve the accuracy of the potential-based brain-computer interface, e.g., P300-based brain-computer interface. The brain-computer interface systems often use symbols or controlled objects as vision stimuli, but what visual stimuli can induce more obvious event-related potential is still unknown. This paper designed three kinds of visual stimuli, i.e., a square, an arrow, and a robot attached with an arrow, to analyze the influence of concreteness degree of the graph on the N200 and P300 potentials, and applied a support vector machine to compare the performance of the brain-computer interface under different stimuli. The results showed that, compared with the square, the robot attached with arrow and the arrow both induced larger N200 potential (P = 1.6 × 10−3, P = 4.2 × 10−2) and longer P300 potential (P = 2.2 × 10−3, P = 1.9 × 10−2) in the frontal area, but the amplitude under the arrow condition is smaller than the one under the robot attached with arrow condition. The robot attached with arrow increased the N200 potential amplitude of the square and arrow from 3.12 μV and 5.19 μV to 7.21 μV (P = 1.6 × 10−3, P = 8.9 × 10−2), and improved the accuracy rate from 59.95%, 61.67% to 74.45% (P = 2.1 × 10−2, P = 1.6 × 10−2), and the information transfer rate from 35.00 bits/min, 35.98 bits/min to 56.71 bits/min (P = 2.6 × 10−2, P = 1.6 × 10−2). This study shows that the concreteness of graphics could affect the N200 potential and the P300 potential. The abstract symbol could represent the meaning and evoke potentials, but the information contained in the concrete robot attached with an arrow is more correlated with the human experience, which is helpful to improve the amplitude. The results may provide new sight in modifying the stimulus interface of the brain-computer interface.
Patients with amyotrophic lateral sclerosis ( ALS ) often have difficulty in expressing their intentions through language and behavior, which prevents them from communicating properly with the outside world and seriously affects their quality of life. The brain-computer interface (BCI) has received much attention as an aid for ALS patients to communicate with the outside world, but the heavy device causes inconvenience to patients in the application process. To improve the portability of the BCI system, this paper proposed a wearable P300-speller brain-computer interface system based on the augmented reality (MR-BCI). This system used Hololens2 augmented reality device to present the paradigm, an OpenBCI device to capture EEG signals, and Jetson Nano embedded computer to process the data. Meanwhile, to optimize the system’s performance for character recognition, this paper proposed a convolutional neural network classification method with low computational complexity applied to the embedded system for real-time classification. The results showed that compared with the P300-speller brain-computer interface system based on the computer screen (CS-BCI), MR-BCI induced an increase in the amplitude of the P300 component, an increase in accuracy of 1.7% and 1.4% in offline and online experiments, respectively, and an increase in the information transfer rate of 0.7 bit/min. The MR-BCI proposed in this paper achieves a wearable BCI system based on guaranteed system performance. It has a positive effect on the realization of the clinical application of BCI.