Ruimin Wang

A Brain-Computer Interface Based on a Few-Channel EEG-fNIRS Bimodal System

With the development of the wearable brain–computer interface (BCI), a few-channel BCI system is necessary for its application to daily life. In this paper, we proposed a bimodal BCI system that uses only a few channels of electroencephalograph (EEG) and functional near-infrared spectroscopy (fNIRS) signals to obtain relatively high accuracy. We developed new approaches for signal acquisition and signal processing to improve the performance of this few-channel BCI system. At the signal acquisition stage, source analysis was applied for both EEG and fNIRS signals to select the optimal channels for bimodal signal collection. At the feature extraction stage, phase-space reconstruction was applied to the selected three-channel EEG signals to expand them into multichannel signals, thus allowing the use of the traditional effective common spatial pattern to extract EEG features. For the fNIRS signal, the Hurst exponents for the selected ten channels were calculated and composed of the fNIRS data feature. At the classification stage, EEG and fNIRS features were joined and classified with the support vector machine. The averaged classification accuracy of 12 participants was 81.2% for the bimodal EEG-fNIRS signals, which was significantly higher than that for either single modality.

An improved multiple LASSO model for steady-state visual evoked potential detection

Improving the classification accuracy in brain–computer interface (BCI) with a short data length is important to increase the BCI system’s information transfer rate. Least absolute shrinkage and selection operator (LASSO) has been examined to be an effective way to detect the steady-state visual evoked potential (SSVEP) signals with a short time window. In this paper, an improved multiple LASSO model for SSVEP detection is proposed, which can process multichannel electroencephalogram (EEG) signals without electrode selection. EEG data from twelve healthy volunteers were used to test the improved multiple LASSO model. Compared with the traditional LASSO model, the improved multiple LASSO model gives a significantly better performance with multichannel EEG data.

A partial least squares-based stimulus frequency recognition model for steady-state visual evoked potentials detection

With shorter calibration times and higher information transfer rates, steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been studied most activity in recent years. Target identification is the ongoing core task in BCI researches, and plays a significant role in practical applications. In order to improve the performance of SSVEP-based BCI system, we proposed a partial least squares (PLS)-based stimulus frequency recognition model for SSVEP detection. Moreover, we compared the proposed method with canonical correlation analysis (CCA) and least absolute shrinkage and selection operator (LASSO) method, respectively. The experiment results showed that PLS can not only extract the SSVEP features effectively, but also can increase the classification accuracies of SSVEP-based BCI systems.

Sinusoidal Signal Assisted Multivariate Empirical Mode Decomposition for Brain–Computer Interfaces

A brain–computer interface (BCI) is a communication approach that permits cerebral activity to control computers or external devices. Brain electrical activity recorded with electroencephalography (EEG) is most commonly used for BCI. Noise-assisted multivariate empirical mode decomposition (NA-MEMD) is a data-driven time-frequency analysis method that can be applied to nonlinear and nonstationary EEG signals for BCI data processing. However, because white Gaussian noise occupies a broad range of frequencies, some redundant components are introduced. To solve this leakage problem, in this study, we propose using a sinusoidal assisted signal that occupies the same frequency ranges as the original signals to improve MEMD performance. To verify the effectiveness of the proposed sinusoidal signal assisted MEMD (SA-MEMD) method, we compared the decomposition performances of MEMD, NA-MEMD, and the proposed SA-MEMD using synthetic signals and a real-world BCI dataset. The spectral decomposition results indicate that the proposed SA-MEMD can avoid the generation of redundant components and over decomposition, thus, substantially reduce the mode mixing and misalignment that occurs in MEMD and NA-MEMD. Moreover, using SA-MEMD as a signal preprocessing method instead of MEMD or NA-MEMD can significantly improve BCI classification accuracy and reduce calculation time, which indicates that SA-MEMD is a powerful spectral decomposition method for BCI.

Action understanding based on a combination of one-versus-rest and one-versus-one multi-classification methods

When people observe the actions of others, they naturally try to understand the underlying intentions. This behavior is called action understanding, and it has an important influence on mental development, language comprehension, and socialization. In this study, we used functional near-infrared spectroscopy (fNIRS) to obtain brain signals related to action understanding and then classified different intentions. Aiming to overcome the drawbacks of traditional multiclass classification methods of one-versus-rest (OVR) and one-versus-one (OVO), in this paper, we propose a new effective method to solve multiclass classification that is a combination of OVR and OVO. Compared with OVO, this new method effectively improved the accuracy of four-class classification from 25% to 48%.

One class support vector machine based filter for improving the classification accuracy of SSVEP BCI

Canonical correlation analysis (CCA) has been proved to be effective in the detection of steady state visual evoked potential (SSVEP) signals. However, the CCA method only chooses the frequency in the reference mode that corresponds to the maximum correlation value as the target. This may make the CCA output less robust. In this study, we propose a one-class support vector machine based filter to filter the sequences of correlation values in the process of the detection of SSVEP signals. The results demonstrate that the classification accuracy improved over different time windows for all subjects and the improvement achieved approximately 10% for some subjects. Moreover, the ratio of instructions that were filtered incorrectly was relative low (less than 5%) if the SSVEP signals were generated effectively.

EEG alpha power change during working memory encoding in adults with different memory performance levels

Working memory (WM) capacity affects our daily life in many ways, and its decrease often associates with neural disorders (e.g. Alzheimers disease). Several studies have confirmed that alpha rhythms play an active role in memory mechanism. In the present study, we designed a digit verbal span experiment to find out that how the alpha power change during working memory encoding, and the relationship between alpha power and individual WM performance. Consistent with previous studies, our results confirm that alpha power is positively related to WM performance. Participants who had higher alpha power during WM encoding achieved better memory performance. We can conclude that the reason is alpha rhythms reflect inhibition of task-irrelevant information. Howbeit, a linear relationship between WM load and alpha power amplitude during memory encoding cannot be deduced in our experiment.

Temporal-Spatial Features of Intention Understanding Based on EEG-fNIRS Bimodal Measurement

Understanding the actions of other people is a key component of social interaction. This paper used an electroencephalography and functional near infrared spectroscopy (EEG-fNIRS) bimodal system to investigate the temporal-spatial features of action intention understanding. We measured brain activation while participants observed three actions: 1) grasping a cup for drinking; 2) grasping a cup for moving; and 3) no meaningful intention. Analysis of EEG maximum standardized current density revealed that brain activation transitioned from the left to the right hemisphere. EEG-fNIRS source analysis results revealed that both the mirror neuron system and theory of mind network are involved in action intention understanding, and the extent to which these two systems are engaged appears to be determined by the clarity of the observed intention. These findings indicate that action intention understanding is a complex and dynamic process.

Evaluating the Feasibility of a Novel Approach for SSVEP Detection Accuracy Improvement Using Phase Shifts

The canonical correlation analysis (CCA), double-partial least-squares (DPLS) methods and least absolute shrinkage and selection operator (LASSO) have been proven effectively in detecting the steady-state visual evoked potential (SSVEP) in SSVEP-based brain-computer interface systems. However, the accuracy of SSVEP classification can be affected by phase shifts of the electroencephalography data, so we explored the possibility of improving SSEVP detection using these methods at different phase shifts. After calculating the accuracy at different phases, we found that the phase shifts could affect the accuracy of SSVEP classification, the classification accuracy could improved about 1.1% mostly using the CCA method, meanwhile the comparison of the three methods was made at the same time and some differences between the CCA, DPLS and LASSO methods at the different phase shifts also be found. The results indicated that on the one hand, the accuracy of SSVEP detection was improved with the change of the phase, but on the other hand, although the three methods could obtain high classification accuracy, the DPLS and LASSO method showed larger fluctuations than the CCA method as the phase of the electroencephalography data of each participant or their average changed.

The combination of CCA and PSDA detection methods in a SSVEP-BCI system

In recent years, based on the steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) have generated significant interest, due to their shorter calibration times and higher information transfer rates. Target identification is the core signal processing task in BCIs. Power spectral density analysis (PSDA) and canonical correlation analysis (CCA) are the most popular and widely used classification methods in SSVEP-BCI systems. In this paper, we first combined these two methods for detecting the SSVEP signals. Moreover, we compared the proposed method with PSDA, CCA method, respectively. The results showed that the proposed method can improve the accuracy and the transfer rate of BCIs.