Reducing False Triggering Caused by Irrelevant Mental Activities in Brain-Computer Interface Based on Motor Imagery.

In recent years, the brain-computer interface (BCI) based on motor imagery (MI) has been considered as a potential post-stroke rehabilitation technology. However, the recognition of MI relies on the event-related desynchronization (ERD) feature, which has poor task specificity. Further, there is the problem of false triggering (irrelevant mental activities recognized as the MI of the target limb). In this paper, we discuss the feasibility of reducing the false triggering rate using a novel paradigm, in which the steady-state somatosensory evoked potential (SSSEP) is combined with the MI (MI-SSSEP). Data from the target (right hand MI) and nontarget task (rest) were used to establish the recognition model, and three kinds of interference tasks were used to test the false triggering performance. In the MI-SSSEP paradigm, ERD and SSSEP features modulated by MI could be used for recognition, while in the MI paradigm, only ERD features could be used. The results showed that the false triggering rate of interference tasks with SSSEP features was reduced to 29.3%, which was far lower than the 55.5% seen under the MI paradigm with ERD features. Moreover, in the MI-SSSEP paradigm, the recognition rate of the target and nontarget task was also significantly improved. Further analysis showed that the specificity of SSSEP was significantly higher than that of ERD (p < 0.05), but the sensitivity was not significantly different. These results indicated that SSSEP modulated by MI could more specifically decode the target task MI, and thereby may have potential in achieving more accurate rehabilitation training.

A BCI based visual-haptic neurofeedback training improves cortical activations and classification performance during motor imagery.

OBJECTIVE: We proposed a brain-computer interface (BCI) based visual-haptic neurofeedback training (NFT) by incorporating synchronous visual scene and proprioceptive electrical stimulation feedback. The goal of this work was to improve sensorimotor cortical activations and classification performance during motor imagery (MI). In addition, their correlations and brain network patterns were also investigated respectively. APPROACH: 64-channel electroencephalographic (EEG) data were recorded in nineteen healthy subjects during MI before and after NFT. During NFT sessions, the synchronous visual-haptic feedbacks were driven by real-time lateralized relative event-related desynchronization (lrERD). MAIN RESULTS: By comparison between previous and posterior control sessions, the cortical activations measured by multi-band (i.e. alpha_1: 8-10 Hz, alpha_2: 11-13 Hz, beta_1: 15-20 Hz and beta_2: 22-28 Hz) absolute ERD powers and lrERD patterns were significantly enhanced after the NFT. The classification performance was also significantly improved, achieving a ~9% improvement and reaching ~85% in mean classification accuracy from a relatively poor performance. Additionally, there were significant correlations between lrERD patterns and classification accuracies. The partial directed coherence based functional connectivity (FC) networks covering the sensorimotor area also showed an increase after the NFT. SIGNIFICANCE: These findings validate the feasibility of our proposed NFT to improve sensorimotor cortical activations and BCI performance during motor imagery. And it is promising to optimize conventional NFT manner and evaluate the effectiveness of motor training.

BCI Monitor Enhances Electroencephalographic and Cerebral Hemodynamic Activations During Motor Training.

Motor imagery-based brain-computer interface (MI-BCI) controlling functional electrical stimulation (FES) is promising for disabled patients to restore their motor functions. However, it remains unclear how much the BCI part can contribute to the functional coupling between the brain and muscle. Specifically, whether it can enhance the cerebral activation for motor training? Here, we investigate the electroencephalographic and cerebral hemodynamic responses for MI-BCI-FES training and MI-FES training, respectively. Twelve healthy subjects were recruited in the motor training study when concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were recorded. Compared with the MI-FES training conditions, the MI-BCI-FES could induce significantly stronger event-related desynchronization (ERD) and blood oxygen response, which demonstrates that BCI indeed plays a functional role in the closed-loop motor training. Therefore, this paper verifies the feasibility of using BCI to train motor functions in a closed-loop manner.

A visual-haptic neurofeedback training improves sensorimotor cortical activations and BCI performance.

Neurofeedback training (NFT) could provide a novel way to investigate or restore the impaired brain function and neuroplasticity. However, it remains unclear how much the different feedback modes can contribute to NFT training. Specifically, whether they can enhance the cortical activations for motor training. To this end, our study proposed a brain-computer interface (BCI) based visual-haptic NFT incorporating synchronous visual scene and proprioceptive electrical stimulation feedback. By comparison between previous and posterior control sessions, the cortical activations measured by multi-band (i.e. alpha_1: 8-10Hz, alpha_2: 11-13Hz, beta_1: 15-20Hz and beta_2: 22-28Hz) lateralized relative event-related desynchronization (lrERD) patterns were significantly enhanced after NFT. And the classification performance was also significantly improved, achieving a ~9% improvement and reaching ~85% in mean classification accuracy from a relatively low MI-BCI performance. These findings validate the feasibility of our proposed visual- haptic NFT approach to improve sensorimotor cortical activations and BCI performance during motor training.

Abnormal EEG Complexity and Functional Connectivity of Brain in Patients with Acute Thalamic Ischemic Stroke.

Ischemic thalamus stroke has become a serious cardiovascular and cerebral disease in recent years. To date the existing researches mostly concentrated on the power spectral density (PSD) in several frequency bands. In this paper, we investigated the nonlinear features of EEG and brain functional connectivity in patients with acute thalamic ischemic stroke and healthy subjects. Electroencephalography (EEG) in resting condition with eyes closed was recorded for 12 stroke patients and 11 healthy subjects as control group. Lempel-Ziv complexity (LZC), Sample Entropy (SampEn), and brain network using partial directed coherence (PDC) were calculated for feature extraction. Results showed that patients had increased mean LZC and SampEn than the controls, which implied the stroke group has higher EEG complexity. For the brain network, the stroke group displayed a trend of weaker cortical connectivity, which suggests a functional impairment of information transmission in cortical connections in stroke patients. These findings suggest that nonlinear analysis and brain network could provide essential information for better understanding the brain dysfunction in the stroke and assisting monitoring or prognostication of stroke evolution.

EEG oscillatory patterns and classification of sequential compound limb motor imagery.

BACKGROUND: A number of studies have been done on movement imagination of motor sequences with a single limb. However, brain oscillatory patterns induced by movement imagination of motor sequences involving multiple limbs have not been reported in recent years. The goal of the present study was to verify the feasibility of application of motor sequences involving multiple limbs to brain-computer interface (BCI) systems based on motor imagery (MI). The changes of EEG patterns and the inter-influence between movements associated with the imagination of motor sequences were also investigated. METHODS: The experiment, where 12 healthy subjects participated, involved one motor sequence with a single limb and three kinds of motor sequences with two or three limbs. The activity involved mental simulation, imagining playing drums with two conditions (60 and 30 beats per minute for the first and second conditions, respectively). RESULTS: Movement imagination of different limbs in the sequence contributed to time-variant event-related desynchronization (ERD) patterns within both mu and beta rhythms, which was more obvious for the second condition compared with the first condition. The ERD values of left/right hand imagery with prior hand imagery were significantly larger than those with prior foot imagery, while the phase locking values (PLVs) between central electrodes and the mesial frontocentral electrode of non-initial movement were significantly larger than those of the initial movement during imagination of motor sequences for both conditions. Classification results showed that the power spectral density (PSD) based method outperformed the multi-class common spatial patterns (multi-CSP) based method: The highest accuracies were 82.86 % and 91.43 %, and the mean values were 65 % and 74.14 % for the first and second conditions, respectively. CONCLUSIONS: This work implies that motor sequences involving multiple limbs can be utilized to build a multimodal classification paradigm in MI-based BCI systems, and that prior movement imagination can result in the changes of neural activities in motor areas during subsequent movement imagination in the process of limb switching.

Randomly dividing homologous samples leads to overinflated accuracies for emotion recognition.

There are numerous studies measuring the brain emotional status by analyzing EEGs under the emotional stimuli that have occurred. However, they often randomly divide the homologous samples into training and testing groups, known as randomly dividing homologous samples (RDHS), despite considering the impact of the non-emotional information among them, which would inflate the recognition accuracy. This work proposed a modified method, the integrating homologous samples (IHS), where the homologous samples were either used to build a classifier, or to be tested. The results showed that the classification accuracy was much lower for the IHS than for the RDHS. Furthermore, a positive correlation was found between the accuracy and the overlapping rate of the homologous samples. These findings implied that the overinflated accuracy did exist in those previous studies where the RDHS method was employed for emotion recognition. Moreover, this study performed a feature selection for the IHS condition based on the support vector machine-recursive feature elimination, after which the average accuracies were greatly improved to 85.71% and 77.18% in the picture-induced and video-induced tasks, respectively.

Analysis of EEG activity in response to binaural beats with different frequencies.

When two coherent sounds with nearly similar frequencies are presented to each ear respectively with stereo headphones, the brain integrates the two signals and produces a sensation of a third sound called binaural beat (BB). Although earlier studies showed that BB could influence behavior and cognition, common agreement on the mechanism of BB has not been reached yet. In this work, we employed Relative Power (RP), Phase Locking Value (PLV) and Cross-Mutual Information (CMI) to track EEG changes during BB stimulations. EEG signals were acquired from 13 healthy subjects. Five-minute BBs with four different frequencies were tested: delta band (1 Hz), theta band (5 Hz), alpha band (10 Hz) and beta band (20 Hz). We observed RP increase in theta and alpha bands and decrease in beta band during delta and alpha BB stimulations. RP decreased in beta band during theta BB, while RP decreased in theta band during beta BB. However, no clear brainwave entrainment effect was identified. Connectivity changes were detected following the variation of RP during BB stimulations. Our observation supports the hypothesis that BBs could affect functional brain connectivity, suggesting that the mechanism of BB-brain interaction is worth further study.

Optimizing visual-to-auditory delay for multimodal BCI speller.

Multimodal spellers combining visual and auditory stimulation have recently gained more attention in ERP-based Brain-Computer Interfaces (BCIs). Most studies found an improved efficiency compared to unimodal paradigms while few have explored the effect of the visual-to-auditory delays on the spelling performance. Here, we study five conditions with different visual-to-auditory delays, in order to find the paradigm that provides the best overall BCI performance. We compared the temporal and spatial binary classification accuracy as well as the grand-averaged classification accuracies over repetitions. Results show that long delays may cause better performance in early time intervals corresponding to negative ERP components, but better overall performance is achieved with short visual-to-auditory delays.

Intelligent algorithm tuning PID method of function electrical stimulation using knee joint angle.

Functional electrical stimulation (FES) could restore motor functions for individuals with spinal cord injury (SCI). By applying electric current pulses, FES system could produce muscle contractions, generate joint torques, and thus, achieve joint movements automatically. Since the muscle system is highly nonlinear and time-varying, feedback control is quite necessary for precision control of the preset action. In the present study, we applied two methods (Proportional Integral Derivative (PID) controller based on Back Propagation (BP) neural network and that based on Genetic Algorithm (GA)), to control the knee joint angle for the FES system, while the traditional Ziegler-Nichols method was used in the control group for comparison. They were tested using a muscle model of the quadriceps. The results showed that intelligent algorithm tuning PID controller displayed superior performance than classic Ziegler-Nichols method with constant parameters. More particularly, PID controller tuned by BP neural network was superior on controlling precision to make the feedback signal track the desired trajectory whose error was less than 1.2°±0.16°, while GA-PID controller, seeking the optimal parameters from multipoint simultaneity, resulted in shortened delay in the response. Both strategies showed promise in application of intelligent algorithm tuning PID methods in FES system.

Evaluation and comparison of effective connectivity during simple and compound limb motor imagery.

Motor imagery (MI) has been demonstrated beneficial in motor rehabilitation in patients with movement disorders. In contrast with simple limb motor imagery, less work was reported about the effective connectivity networks of compound limb motor imagery which involves several parts of limbs. This work aimed to investigate the differences of information flow patterns between simple limb motor imagery and compound limb motor imagery. Ten subjects participated in the experiment involving three tasks of simple limb motor imagery (left hand, right hand, feet) and three tasks of compound limb motor imagery (both hands, left hand combined with right foot, right hand combined with left foot). The causal interactions among different neural regions were evaluated by Short-time Directed Transfer Function (SDTF). Quite different from the networks of simple limb motor imagery, more effective interactions overlying larger brain regions were observed during compound limb motor imagery. These results imply that there exist significant differences in the patterns of EEG activity flow between simple limb motor imagery and compound limb motor imagery, which present more complex networks and could be utilized in motor rehabilitation for more benefit in patients with movement disorders.

EEG feature comparison and classification of simple and compound limb motor imagery.

BACKGROUND: Motor imagery can elicit brain oscillations in Rolandic mu rhythm and central beta rhythm, both originating in the sensorimotor cortex. In contrast with simple limb motor imagery, less work was reported about compound limb motor imagery which involves several parts of limbs. The goal of this study was to investigate the differences of the EEG patterns between simple limb motor imagery and compound limb motor imagery, and discuss the separability of multiple types of mental tasks. METHODS: Ten subjects participated in the experiment involving three tasks of simple limb motor imagery (left hand, right hand, feet), three tasks of compound limb motor imagery (both hands, left hand combined with right foot, right hand combined with left foot) and rest state. Event-related spectral perturbation (ERSP), power spectral entropy (PSE) and spatial distribution coefficient were adopted to analyze these seven EEG patterns. Then three algorithms of modified multi-class common spatial patterns (CSP) were used for feature extraction and classification was implemented by support vector machine (SVM). RESULTS: The induced event-related desynchronization (ERD) affects more components within both alpha and beta bands resulting in more broad ERD bands at electrode positions C3, Cz and C4 during left/right hand combined with contralateral foot imagery, whose PSE values are significant higher than that of simple limb motor imagery. From the topographical distribution, simultaneous imagination of upper limb and contralateral lower limb certainly contributes to the activation of more areas on cerebral cortex. Classification result shows that multi-class stationary Tikhonov regularized CSP (Multi-sTRCSP) outperforms other two multi-class CSP methods, with the highest accuracy of 84% and mean accuracy of 70%. CONCLUSIONS: The work implies that there exist the separable differences between simple limb motor imagery and compound limb motor imagery, which can be utilized to build a multimodal classification paradigm in motor imagery based brain-computer interface (BCI) systems.

A gait stability investigation into FES-assisted paraplegic walking based on the walker tipping index.

The gait outcome measures used in clinical trials of paraplegic locomotor training determine the effectiveness of improved walking function assisted by the functional electrical stimulation (FES) system. Focused on kinematic, kinetic or physiological changes of paraplegic patients, traditional methods cannot quantify the walking stability or identify the unstable factors of gait in real time. Up until now, the published studies on dynamic gait stability for the effective use of FES have been limited. In this paper, the walker tipping index (WTI) was used to analyze and process gait stability in FES-assisted paraplegic walking. The main instrument was a specialized walker dynamometer system based on a multi-channel strain-gauge bridge network fixed on the frame of the walker. This system collected force information for the handle reaction vector between the patient's upper extremities and the walker during the walking process; the information was then converted into walker tipping index data, which is an evaluation indicator of the patient's walking stability. To demonstrate the potential usefulness of WTI in gait analysis, a preliminary clinical trial was conducted with seven paraplegic patients who were undergoing FES-assisted walking training and seven normal control subjects. The gait stability levels were quantified for these patients under different stimulation patterns and controls under normal walking with knee-immobilization through WTI analysis. The results showed that the walking stability in the FES-assisted paraplegic group was worse than that in the control subject group, with the primary concern being in the anterior-posterior plane. This new technique is practical for distinguishing useful gait information from the viewpoint of stability, and may be further applied in FES-assisted paraplegic walking rehabilitation.