Steady-State movement related potentials for brain–computer interfacing

An approach for brain-computer interfacing (BCI) by analysis of steady-state movement related potentials (ssMRPs) produced during rhythmic finger movements is proposed in this paper. The neurological background of ssMRPs is briefly reviewed. Averaged ssMRPs represent the development of a lateralized rhythmic potential, and the energy of the EEG signals at the finger tapping frequency can be used for single-trial ssMRP classification. The proposed ssMRP-based BCI approach is tested using the classic Fisher's linear discriminant classifier. Moreover, the influence of the current source density transform on the performance of BCI system is investigated. The averaged correct classification rates (CCRs) as well as averaged information transfer rates (ITRs) for different sliding time windows are reported. Reliable single-trial classification rates of 88%-100% accuracy are achievable at relatively high ITRs. Furthermore, we have been able to achieve CCRs of up to 93% in classification of the ssMRPs recorded during imagined rhythmic finger movements. The merit of this approach is in the application of rhythmic cues for BCI, the relatively simple recording setup, and straightforward computations that make the real-time implementations plausible

EEG and ECoG features for Brain Computer Interface in Stroke Rehabilitation

The ability of non-invasive Brain-Computer Interface (BCI) to control an exoskeleton was used for motor rehabilitation in stroke patients or as an assistive device for the paralyzed. However, there is still a need to create a more reliable BCI that could be used to control several degrees of Freedom (DoFs) that could improve rehabilitation results. Decoding different movements from the same limb, high accuracy and reliability are some of the main difficulties when using conventional EEG-based BCIs and the challenges we tackled in this thesis. In this PhD thesis, we investigated that the classification of several functional hand reaching movements from the same limb using EEG is possible with acceptable accuracy. Moreover, we investigated how the recalibration could affect the classification results. For this reason, we tested the recalibration in each multi-class decoding for within session, recalibrated between-sessions, and between sessions. It was shown the great influence of recalibrating the generated classifier with data from the current session to improve stability and reliability of the decoding. Moreover, we used a multiclass extension of the Filter Bank Common Spatial Patterns (FBCSP) to improve the decoding accuracy based on features and compared it to our previous study using CSP. Sensorimotor-rhythm-based BCI systems have been used within the same frequency ranges as a way to influence brain plasticity or controlling external devices. However, neural oscillations have shown to synchronize activity according to motor and cognitive functions. For this reason, the existence of cross-frequency interactions produces oscillations with different frequencies in neural networks. In this PhD, we investigated for the first time the existence of cross-frequency coupling during rest and movement using ECoG in chronic stroke patients. We found that there is an exaggerated phase-amplitude coupling between the phase of alpha frequency and the amplitude of gamma frequency, which can be used as feature or target for neurofeedback interventions using BCIs. This coupling has been also reported in another neurological disorder affecting motor function (Parkinson and dystonia) but, to date, it has not been investigated in stroke patients. This finding might change the future design of assistive or therapeuthic BCI systems for motor restoration in stroke patients

Synchronization of Slow Cortical Rhythms During Motor Imagery-Based Brain–Machine Interface Control

Modulation of sensorimotor rhythm (SMR) power, a rhythmic brain oscillation physiologically linked to motor imagery, is a popular Brain–Machine Interface (BMI) paradigm, but its interplay with slower cortical rhythms, also involved in movement preparation and cognitive processing, is not entirely understood. In this study, we evaluated the changes in phase and power of slow cortical activity in delta and theta bands, during a motor imagery task controlled by an SMR-based BMI system. In Experiment I, EEG of 20 right-handed healthy volunteers was recorded performing a motor-imagery task using an SMR-based BMI controlling a visual animation, and during task-free intervals. In Experiment II, 10 subjects were evaluated along five daily sessions, while BMI-controlling same visual animation, a buzzer, and a robotic hand exoskeleton. In both experiments, feedback received from the controlled device was proportional to SMR power (11–14 Hz) detected by a real-time EEG-based system. Synchronization of slow EEG frequencies along the trials was evaluated using inter-trial-phase coherence (ITPC). Results: cortical oscillations of EEG in delta and theta frequencies synchronized at the onset and at the end of both active and task-free trials; ITPC was significantly modulated by feedback sensory modality received during the tasks; and ITPC synchronization progressively increased along the training. These findings suggest that phase-locking of slow rhythms and resetting by sensory afferences might be a functionally relevant mechanism in cortical control of motor function. We propose that analysis of phase synchronization of slow cortical rhythms might also improve identification of temporal edges in BMI tasks and might help to develop physiological markers for identification of context task switching and practice-related changes in brain function, with potentially important implications for design and monitoring of motor imagery-based BMI systems, an emerging tool in neurorehabilitation of stro

Perception and cognition of cues Used in synchronous Brain–computer interfaces Modify electroencephalographic Patterns of control Tasks

A motor imagery (MI)-based brain–computer interface (BCI) is a system that enables humans to interact with their environment by translating their brain signals into control commands for a target device. In particular, synchronous BCI systems make use of cues to trigger the motor activity of interest. So far, it has been shown that electroencephalographic (EEG) patterns before and after cue onset can reveal the user cognitive state and enhance the discrimination of MI-related control tasks. However, there has been no detailed investigation of the nature of those EEG patterns. We, therefore, propose to study the cue effects on MI-related control tasks by selecting EEG patterns that best discriminate such control tasks, and analyzing where those patterns are coming from. The study was carried out using two methods: standard and all-embracing. The standard method was based on sources (recording sites, frequency bands, and time windows), where the modulation of EEG signals due to motor activity is typically detected. The all-embracing method included a wider variety of sources, where not only motor activity is reflected. The findings of this study showed that the classification accuracy (CA) of MI-related control tasks did not depend on the type of cue in use. However, EEG patterns that best differentiated those control tasks emerged from sources well defined by the perception and cognition of the cue in use. An implication of this study is the possibility of obtaining different control commands that could be detected with the same accuracy. Since different cues trigger control tasks that yield similar CAs, and those control tasks produce EEG patterns differentiated by the cue nature, this leads to accelerate the brain–computer communication by having a wider variety of detectable control commands. This is an important issue for Neuroergonomics research because neural activity could not only be used to monitor the human mental state as is typically done, but this activity might be also employed to control the system of interest

CLOSED-LOOP AFFERENT NERVE ELECTRICAL STIMULATION FOR REHABILITATION OF HAND FUNCTION IN SUBJECTS WITH INCOMPLETE SPINAL CORD INJURY

Peripheral nerve stimulation (PNS) is commonly used to promote use-dependent cortical plasticity for rehabilitation of motor function in spinal cord injury. Pairing transcranial magnetic stimulation (TMS) with PNS has been shown to increase motor evoked potentials most when the two stimuli are timed to arrive in the cortex simultaneously. This suggests that a mechanism of timing-dependent plasticity (TDP) may be a more effective method of promoting motor rehabilitation. The following thesis is the result of applying a brain-computer interface to apply PNS in closed-loop simultaneously to movement intention onset as measured by EEG of the sensorimotor cortex to test whether TDP can be induced in incomplete spinal cord injured individuals with upper limb motor impairment. 4 motor incomplete SCI subjects have completed 12 sessions of closed-loop PNS delivered over 4-6 weeks. Benefit was observed for every subject although not consistently across metrics. 3 out of 4 subjects exhibited increased maximum voluntary contraction force (MVCF) between first and last interventions for one or both hands. TMS-measured motor map volume increased for both hemispheres in one subject, and TMS center of gravity shifted in 3 subjects consistent with studies in which motor function improved or was restored. These observations suggest that rehabilitation using similar designs for responsive stimulation could improve motor impairment in SCI

Influence of attention alternation on movement-related cortical potentials in healthy individuals and stroke patients.

OBJECTIVE: In this study, we analyzed the influence of artificially imposed attention variations using the auditory oddball paradigm on the cortical activity associated to motor preparation/execution. METHODS: EEG signals from Cz and its surrounding channels were recorded during three sets of ankle dorsiflexion movements. Each set was interspersed with either a complex or a simple auditory oddball task for healthy participants and a complex auditory oddball task for stroke patients. RESULTS: The amplitude of the movement-related cortical potentials (MRCPs) decreased with the complex oddball paradigm, while MRCP variability increased. Both oddball paradigms increased the detection latency significantly (p<0.05) and the complex paradigm decreased the true positive rate (TPR) (p=0.04). In patients, the negativity of the MRCP decreased while pre-phase variability increased, and the detection latency and accuracy deteriorated with attention diversion. CONCLUSION: Attention diversion has a significant influence on MRCP features and detection parameters, although these changes were counteracted by the application of the laplacian method. SIGNIFICANCE: Brain-computer interfaces for neuromodulation that use the MRCP as the control signal are robust to changes in attention. However, attention must be monitored since it plays a key role in plasticity induction. Here we demonstrate that this can be achieved using the single channel Cz

Neural Mechanisms of Sensory Integration: Frequency Domain Analysis of Spike and Field Potential Activity During Arm Position Maintenance with and Without Visual Feedback

abstract: Understanding where our bodies are in space is imperative for motor control, particularly for actions such as goal-directed reaching. Multisensory integration is crucial for reducing uncertainty in arm position estimates. This dissertation examines time and frequency-domain correlates of visual-proprioceptive integration during an arm-position maintenance task. Neural recordings were obtained from two different cortical areas as non-human primates performed a center-out reaching task in a virtual reality environment. Following a reach, animals maintained the end-point position of their arm under unimodal (proprioception only) and bimodal (proprioception and vision) conditions. In both areas, time domain and multi-taper spectral analysis methods were used to quantify changes in the spiking, local field potential (LFP), and spike-field coherence during arm-position maintenance. In both areas, individual neurons were classified based on the spectrum of their spiking patterns. A large proportion of cells in the SPL that exhibited sensory condition-specific oscillatory spiking in the beta (13-30Hz) frequency band. Cells in the IPL typically had a more diverse mix of oscillatory and refractory spiking patterns during the task in response to changing sensory condition. Contrary to the assumptions made in many modelling studies, none of the cells exhibited Poisson-spiking statistics in SPL or IPL. Evoked LFPs in both areas exhibited greater effects of target location than visual condition, though the evoked responses in the preferred reach direction were generally suppressed in the bimodal condition relative to the unimodal condition. Significant effects of target location on evoked responses were observed during the movement period of the task well. In the frequency domain, LFP power in both cortical areas was enhanced in the beta band during the position estimation epoch of the task, indicating that LFP beta oscillations may be important for maintaining the ongoing state. This was particularly evident at the population level, with clear increase in alpha and beta power. Differences in spectral power between conditions also became apparent at the population level, with power during bimodal trials being suppressed relative to unimodal. The spike-field coherence showed confounding results in both the SPL and IPL, with no clear correlation between incidence of beta oscillations and significant beta coherence.Dissertation/ThesisDoctoral Dissertation Biomedical Engineering 201

Task switching in the prefrontal cortex

The overall goal of this dissertation is to elucidate the cellular and circuit mechanisms underlying flexible behavior in the prefrontal cortex. We are often faced with situations in which the appropriate behavior in one context is inappropriate in others. If these situations are familiar, we can perform the appropriate behavior without relearning how the context relates to the behavior — an important hallmark of intelligence. Neuroimaging and lesion studies have shown that this dynamic, flexible process of remapping context to behavior (task switching) is dependent on prefrontal cortex, but the precise contributions and interactions of prefrontal subdivisions are still unknown. This dissertation investigates two prefrontal areas that are thought to be involved in distinct, but complementary executive roles in task switching — the dorsolateral prefrontal cortex (dlPFC) and the anterior cingulate cortex (ACC). Using electrophysiological recordings from macaque monkeys, I show that synchronous network oscillations in the dlPFC provide a mechanism to flexibly coordinate context representations (rules) between groups of neurons during task switching. Then, I show that, wheras the ACC neurons can represent rules at the cellular level, they do not play a significant role in switching between contexts — rather they seem to be more related to errors and motivational drive. Finally, I develop a set of web-enabled interactive visualization tools designed to provide a multi-dimensional integrated view of electrophysiological datasets. Taken together, these results contribute to our understanding of task switching by investigating new mechanisms for coordination of neurons in prefrontal cortex, clarifying the roles of prefrontal subdivisions during task switching, and providing visualization tools that enhance exploration and understanding of large, complex and multi-scale electrophysiological data