Huaijian Zhang

Improved recognition of error related potentials through the use of brain connectivity features

Brain error processing plays a key role in goal-directed behavior and learning in human brain. Directed transfer function (DTF) on EEG signal brings unique features for discrimination between correct and error cases in brain-computer interface (BCI) system. We describe the first application of brain connectivity features for recognizing error-related signals in non-invasive BCI. EEG signal were recorded from 16 human subjects when they monitored stimuli moving in either correct or erroneous direction. Classification performance using waveform features, brain connectivity features and their combination were compared. The result of combined features yielded highest classification accuracy, 0:85. In addition, we also show that brain connectivity at theta band around 200ms after stimuli carry highly discriminant information between error and correct trials. This paper provides evidence that the use of connectivity features improve the performance of an EEG based BCI.

EEG-based decoding of error-related brain activity in a real-world driving task.

OBJECTIVES Recent studies have started to explore the implementation of brain-computer interfaces (BCI) as part of driving assistant systems. The current study presents an EEG-based BCI that decodes error-related brain activity. Such information can be used, e.g., to predict drivers intended turning direction before reaching road intersections. APPROACH We executed experiments in a car simulator (N = 22) and a real car (N = 8). While subject was driving, a directional cue was shown before reaching an intersection, and we classified the presence or not of an error-related potentials from EEG to infer whether the cued direction coincided with the subjects intention. In this protocol, the directional cue can correspond to an estimation of the driving direction provided by a driving assistance system. We analyzed ERPs elicited during normal driving and evaluated the classification performance in both offline and online tests. RESULTS An average classification accuracy of 0.698 ± 0.065 was obtained in offline experiments in the car simulator, while tests in the real car yielded a performance of 0.682 ± 0.059. The results were significantly higher than chance level for all cases. Online experiments led to equivalent performances in both simulated and real car driving experiments. These results support the feasibility of decoding these signals to help estimating whether the drivers intention coincides with the advice provided by the driving assistant in a real car. SIGNIFICANCE The study demonstrates a BCI system in real-world driving, extending the work from previous simulated studies. As far as we know, this is the first online study in real car decoding drivers error-related brain activity. Given the encouraging results, the paradigm could be further improved by using more sophisticated machine learning approaches and possibly be combined with applications in intelligent vehicles.

Discriminant Brain Connectivity Patterns of Performance Monitoring at Average and Single-Trial Levels

Electrophysiological and neuroimaging evidence suggest the existence of common mechanisms for monitoring erroneous events, independent of the source of errors. Previous works have described modulations of theta activity in the medial frontal cortex elicited by either self-generated errors or erroneous feedback. In turn, similar patterns have recently been reported to appear after the observation of external errors. We report cross-regional interactions after observation of errors at both average and single-trial levels. We recorded scalp electroencephalography (EEG) signals from 15 subjects while monitoring the movement of a cursor on a computer screen. Connectivity patterns, estimated using multivariate auto-regressive models, show increased error-related modulations of the information transfer in the theta and alpha bands between frontocentral and frontolateral areas. Conversely, a decrease of connectivity in the beta band is also observed. These network patterns are similar to those elicited by self-generated errors. However, since no motor response is required, they appear to be related to intrinsic mechanisms of error processing, instead of being linked to co-activation of motor areas. Noticeably, we demonstrate that cross-regional interaction patterns can be estimated on a trial-by-trial basis. These trial-specific patterns, consistent with the multi-trial analysis, convey discriminant information on whether a trial was elicited by observation of an erroneous action. Overall, our study supports the role of frequency-specific modulations in the medial frontal cortex in coordinating cross-regional activity during cognitive monitoring at a single-trial basis.

Inferring driver's turning direction through detection of error related brain activity

This work presents EEG-based Brain-computer interface (BCI) that uses error related brain activity to improve the prediction of drivers intended turning direction. In experiments while subjects drive in a realistic car simulator, we show a directional cue before reaching intersection, and analyze error related EEG potential to infer if the presented direction coincides with the drivers intention. In this protocol, the directional cue provides an initial estimation of the driving direction (based on EEG, environmental or previous driving habits), and we focus on the recognition of error-potentials it may elicit. Experiments with 7 healthy human subjects yield an average classification 0.69±0.16, which confirms the feasibility of decoding these signals to help estimating drivers turning direction. This study can be further exploited by intelligent cars to tune their driving assistant systems to improve their performance and enhance the driving experience.

Behavioral and cortical effects during attention driven brain-computer interface operations in spatial neglect: A feasibility case study

During the last years, several studies have suggested that Brain-Computer Interface (BCI) can play a critical role in the field of motor rehabilitation. In this case report, we aim to investigate the feasibility of a covert visuospatial attention (CVSA) driven BCI in three patients with left spatial neglect (SN). We hypothesize that such a BCI is able to detect attention task-specific brain patterns in SN patients and can induce significant changes in their abnormal cortical activity (α-power modulation, feature recruitment, and connectivity). The three patients were asked to control online a CVSA BCI by focusing their attention at different spatial locations, including their neglected (left) space. As primary outcome, results show a significant improvement of the reaction time in the neglected space between calibration and online modalities (p < 0.01) for the two out of three patients that had the slowest initial behavioral response. Such an evolution of reaction time negatively correlates (p < 0.05) with an increment of the Individual α-Power computed in the pre-cue interval. Furthermore, all patients exhibited a significant reduction of the inter-hemispheric imbalance (p < 0.05) over time in the parieto-occipital regions. Finally, analysis on the inter-hemispheric functional connectivity suggests an increment across modalities for regions in the affected (right) hemisphere and decrement for those in the healthy. Although preliminary, this feasibility study suggests a possible role of BCI in the therapeutic treatment of lateralized, attention-based visuospatial deficits.

Towards Implementation of Motor Imagery using Brain Connectivity Features

This study aims to explore modulation of the connectivity pattern when people perform left hand versus right hand motor imagery and probe the feasibility of adopting connectivity information to discriminate these tasks. Nine subjects were recorded with 16-channel EEG system, covering sensorimotor cortex. Non-normalized directed transfer function (DTF) is used to obtain the brain connectivity between EEG electrodes. The results demonstrate that the modulations of intrahemispheric and interhemispheric information flows are not identical during left and right hand motor imageries. Particularly, the mu rhythm is highly modulated in intrahemispheric brain interactions, whereas the high frequency bands are more related with distant interhemispheric brain interactions. Furthermore, classification results suggest that the DTF features bring additional informative features for the classification between two tasks.

Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke

Brain-computer interfaces (BCI) are used in stroke rehabilitation to translate brain signals into intended movements of the paralyzed limb. However, the efficacy and mechanisms of BCI-based therapies remain unclear. Here we show that BCI coupled to functional electrical stimulation (FES) elicits significant, clinically relevant, and lasting motor recovery in chronic stroke survivors more effectively than sham FES. Such recovery is associated to quantitative signatures of functional neuroplasticity. BCI patients exhibit a significant functional recovery after the intervention, which remains 6–12 months after the end of therapy. Electroencephalography analysis pinpoints significant differences in favor of the BCI group, mainly consisting in an increase in functional connectivity between motor areas in the affected hemisphere. This increase is significantly correlated with functional improvement. Results illustrate how a BCI–FES therapy can drive significant functional recovery and purposeful plasticity thanks to contingent activation of body natural efferent and afferent pathways.Brain-computer interface (BCI) can improve motor skills on stroke patients. This study shows that BCI-controlled neuromuscular electrical stimulation therapy can cause cortical reorganization due to activation of efferent and afferent pathways, and this effect can be long lasting in a brain region specific manner.

Brain Correlates of Lane Changing Reaction Time in Simulated Driving

Psychophysical studies have reported correlation between neural activity in frontal and parietal areas and subjects reaction time in simple tasks. Here we study whether similar correlates can also be identified in drivers electroencephalography (EEG) activity when they perform steering actions triggered by exogenous stimuli (e.g. Obstacles along the road). We report analysis of the EEG signals of fifteen subjects while they drive in a realistic car simulator. We found that the peak latency of the event-related potentials in frontal and parietal areas significantly correlates with the onset of the steering behavior. Similarly, modulations of the power in the theta band (4-8 Hz) prior to the action also correlates with the reaction times. These results provide evidence of reliable neural markers of the drivers response variability.

Detecting Cognitive States for Enhancing Driving Experience

Intelligent cars exploit environmental information to support drivers by providing extra information and assisting complex maneouvers. They can also take into account the internal state of the driver by means of decoding cognition-related brain activity. Here we show the feasibility of successfully classify EEG correlates of anticipation, movement preparation and error processing while subjects drive in a realistic car simulator.

Human EEG reveals distinct neural correlates of power and precision grasping types

ABSTRACT Hand grasping is a sophisticated motor task that has received much attention by the neuroscientific community, which demonstrated how grasping activates a network involving parietal, pre‐motor and motor cortices using fMRI, ECoG, LFPs and spiking activity. Yet, there is a need for a more precise spatio‐temporal analysis as it is still unclear how these brain activations over large cortical areas evolve at the sub‐second level. In this study, we recorded ten human participants (1 female) performing visually‐guided, self‐paced reaching and grasping with precision or power grips. Following the results, we demonstrate the existence of neural correlates of grasping from broadband EEG in self‐paced conditions and show how neural correlates of precision and power grasps differentially evolve as grasps unfold. 100ms before the grasp is secured, bilateral parietal regions showed increasingly differential patterns. Afterwards, sustained differences between both grasps occurred over the bilateral motor and parietal regions, and medial pre‐frontal cortex. Furthermore, these differences were sufficiently discriminable to allow single‐trial decoding with 70% decoding performance. Functional connectivity revealed differences at the network level between grasps in fronto‐parietal networks, in terms of upper‐alpha cortical oscillatory power with a strong involvement of ipsilateral hemisphere. Our results supported the existence of fronto‐parietal recurrent feedback loops, with stronger interactions for precision grips due to the finer motor control required for this grasping type. HIGHLIGHTSWe present a broadband EEG study of precision vs power grips at the subsecond level.EEG neural correlates of self‐paced grasping can be detected in single trials.EEG connectivity supports existence of fronto‐parietal recurrent feedback loops.