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Abstract. Brain-Computer Interfaces based on electrocorticography (ECoG) or electroencephalography (EEG), in combination with robot-assisted active physical therapy, may support traditional rehabilitation procedures for patients with severe motor impairment due to cerebrovascular brain damage caused by stroke. In this short report, we briefly review the state-of-the art in this exciting new field, give an overview of the work carried out at the Max Planck Institute for Biological Cybernetics and the University of Tübingen, and discuss challenges that need to be addressed in order to move from basic research to clinical studies. Current rehabilitation methods for patients with severe motor impairment due to cerebrovascular brain damage are limited in providing significant long-term functional recovery. In stroke patients, functional recovery beyond one year post-stroke is rare (Johnston et al. [2004]), and functional independence often displays a long-term decline (Dhamoon et al. [2009]). As such, novel strategies in stroke rehabilitation are required. Robot-assisted physical therapy (Riener et al. [2005]) and motor imagery (Dijkerman et al. [2004], Page et al. [2007]) have been shown to be beneficial in stroke rehabilitation

Functional definition of seizure provides new insight into post-traumatic epileptogenesis

Experimental animals’ seizures are often defined arbitrarily based on duration, which may lead to misjudgement of the syndrome and failure to develop a cure. We employed a functional definition of seizures based on the clinical practice of observing epileptiform electrocorticography and simultaneous ictal behaviour, and examined post-traumatic epilepsy induced in rats by rostral parasagittal fluid percussion injury and epilepsy patients evaluated with invasive monitoring. We showed previously that rostral parasagittal fluid percussion injury induces different types of chronic recurrent spontaneous partial seizures that worsen in frequency and duration over the months post injury. However, a remarkable feature of rostral parasagittal fluid percussion injury is the occurrence, in the early months post injury, of brief (<2 s) focal, recurrent and spontaneous epileptiform electrocorticography events (EEEs) that are never observed in sham-injured animals and have electrographic appearance similar to the onset of obvious chronic recurrent spontaneous partial seizures. Simultaneous epidural-electrocorticography and scalp-electroencephalography recordings in the rat demonstrated that these short EEEs are undetectable by scalp electrocorticography. Behavioural analysis performed blinded to the electrocorticography revealed that (i) brief EEEs lasting 0.8–2 s occur simultaneously with behavioural arrest; and (ii) while behavioural arrest is part of the rat's behavioural repertoire, the probability of behavioural arrest is greatly elevated during EEEs. Moreover, spectral analysis showed that EEEs lasting 0.8–2 s occurring during periods of active behaviour with dominant theta activity are immediately followed by loss of such theta activity. We thus conclude that EEEs lasting 0.8–2 s are ictal in the rat. We demonstrate that the assessment of the time course of fluid percussion injury-induced epileptogenesis is dramatically biased by the definition of seizure employed, with common duration-based arbitrary definitions resulting in artificially prolonged latencies for epileptogenesis. Finally, we present four human examples of electrocorticography capturing short (<2 s), stereotyped, neocortically generated EEEs that occurred in the same ictal sites as obvious complex partial seizures, were electrographically similar to rat EEEs and were not noted during scalp electroencephalography. When occurring in the motor cortex, these short EEEs were accompanied by ictal behaviour detectable with simultaneous surface electromyography. These data demonstrate that short (<2 s) focal recurrent spontaneous EEEs are seizures in both rats and humans, that they are undetectable by scalp electroencephalography, and that they are typically associated with subtle and easily missed behavioural correlates. These findings define the earliest identifiable markers of progressive post-traumatic epilepsy in the rat, with implications for mechanistic and prophylactic studies, and should prompt a re-evaluation of the concept of post-traumatic silent period in both animals and humans

Partial Maximum Correntropy Regression for Robust Trajectory Decoding from Noisy Epidural Electrocorticographic Signals

The Partial Least Square Regression (PLSR) exhibits admirable competence for predicting continuous variables from inter-correlated brain recordings in the brain-computer interface. However, PLSR is in essence formulated based on the least square criterion, thus, being non-robust with respect to noises. The aim of this study is to propose a new robust implementation for PLSR. To this end, the maximum correntropy criterion (MCC) is used to propose a new robust variant of PLSR, called as Partial Maximum Correntropy Regression (PMCR). The half-quadratic optimization is utilized to calculate the robust projectors for the dimensionality reduction, and the regression coefficients are optimized by a fixed-point approach. We evaluate the proposed PMCR with a synthetic example and the public Neurotycho electrocorticography (ECoG) datasets. The extensive experimental results demonstrate that, the proposed PMCR can achieve better prediction performance than the conventional PLSR and existing variants with three different performance indicators in high-dimensional and noisy regression tasks. PMCR can suppress the performance degradation caused by the adverse noise, ameliorating the decoding robustness of the brain-computer interface

Brain-Computer Interfaces using Electrocorticography and Surface Stimulation

The brain connects to, modulates, and receives information from every organ in the body. As such, brain-computer interfaces (BCIs) have vast potential for diagnostics, medical therapies, and even augmentation or enhancement of normal functions. BCIs provide a means to explore the furthest corners of what it means to think, to feel, and to act—to experience the world and to be who you are. This work focuses on the development of a chronic bi-directional BCI for sensorimotor restoration through the use of separable frequency bands for recording motor intent and providing sensory feedback via electrocortical stimulation. Epidural cortical surface electrodes are used to both record electrocorticographic (ECoG) signals and provide stimulation without adverse effects associated with penetration through the protective dural barrier of brain. Chronic changes in electrode properties and signal characteristics are discussed, which inform optimal electrode designs and co-adaptive algorithms for decoding high-dimensional information. Additionally, a multi-layered approach to artifact suppression is presented, which includes a systems-level design of electronics, signal processing, and stimulus waveforms. The results of this work are relevant to a wider range of applications beyond ECoG and BCIs that involve closed-loop recording and stimulation throughout the body. By enabling simultaneous recording and stimulation through the techniques described here, responsive therapies can be developed that are tuned to individual patients and provide precision therapies at exactly the right place and time. This has the potential to improve targeted therapeutic outcomes while reducing undesirable side effects

Neural Adaptation and the Effect of Interelectrode Spacing on Epidural Electrocorticography for Brain-Computer Interfaces

Electrocorticography: ECoG) is increasingly being identified as a safe and reliable recording technique for both Brain-Computer Interface: BCI) applications as well as neurophysiology studies. This thesis describes some of the first real-time closed-loop BCI studies of chronic ECoG in non-human primates. Epidural microECoG electrodes developed in our lab were implanted in three monkeys with the electrode array centered over primary motor cortex: M1). Monkeys were then trained to perform a one-dimensional BCI task. The BCI control scheme was independent of any prior screening for task-related activity. All three monkeys successfully learned to perform the task with multiple control configurations and each time gained significant performance in 10 days or less. Interelectrode distance between control electrodes was also tested for three different distances. 15 and 9 mm spacing resulted in equivalent performance while 3 mm saw a moderate but significant degradation in performance. Finally, post hoc analysis was performed to analyze various decoding parameters. While decoding parameters were generally well matched to the observed signals, several potential decoding improvements were identified. Overall, these results demonstrate the feasibility of epidural ECoG BCIs, highlight the importance of neural adaptation for BCI control, and quantify various metrics of a current ECoG BCI system to drive further studies

An Electrocorticographic Brain Interface in an Individual with Tetraplegia

Brain-computer interface (BCI) technology aims to help individuals with disability to control assistive devices and reanimate paralyzed limbs. Our study investigated the feasibility of an electrocorticography (ECoG)-based BCI system in an individual with tetraplegia caused by C4 level spinal cord injury. ECoG signals were recorded with a high-density 32-electrode grid over the hand and arm area of the left sensorimotor cortex. The participant was able to voluntarily activate his sensorimotor cortex using attempted movements, with distinct cortical activity patterns for different segments of the upper limb. Using only brain activity, the participant achieved robust control of 3D cursor movement. The ECoG grid was explanted 28 days post-implantation with no adverse effect. This study demonstrates that ECoG signals recorded from the sensorimotor cortex can be used for real-time device control in paralyzed individuals

ECoG correlates of visuomotor transformation, neural plasticity, and application to a force-based brain computer interface

Electrocorticography: ECoG) has gained increased notoriety over the past decade as a possible recording modality for Brain-Computer Interface: BCI) applications that offers a balance of minimal invasiveness to the patient in addition to robust spectral information over time. More recently, the scale of ECoG devices has begun to shrink to the order of micrometer diameter contacts and millimeter spacings with the intent of extracting more independent signals for BCI control within less cortical real-estate. However, most control signals to date, whether within the field of ECoG or any of the more seasoned recording techniques, have translated their control signals to kinematic control parameters: i.e. position or velocity of an object) which may not be practical for certain BCI applications such as functional neuromuscular stimulation: FNS). Thus, the purpose of this dissertation was to present a novel application of ECoG signals to a force-based control algorithm and address its feasibility for such a BCI system. Micro-ECoG arrays constructed from thin-film polyimide were implanted epidurally over areas spanning premotor, primary motor, and parietal cortical areas of two monkeys: three hemispheres, three arrays). Monkeys first learned to perform a classic center-out task using a brain signal-to-velocity mapping for control of a computer cursor. The BCI algorithm utilized day-to-day adaptation of the decoding model to match the task intention of the monkeys with no need for pre-screeening of movement-related ECoG signals. Using this strategy, subjects showed notable 2-D task profiency and increased task-related modulation of ECoG features within five training sessions. After fixing the last model trained for velocity control of the cursor, the monkeys then utilized this decoding model to control the acceleration of the cursor in the same center-out task. Cursor movement profiles under this mapping paralleled those demonstrated using velocity control, and neural control signal profiles revealed the monkeys actively accelerated and decelerated the cursor within a limited time window: 1-1.5 seconds). The fixed BCI decoding model was recast once again to control the force on a virtual cursor in a novel mass-grab task. This task required targets not only to reach to peripheral targets but also account for an additional virtual mass as they grabbed each target and moved it to a second target location in the presence of the external force of gravity. Examination of the ensemble control signals showed neural adaptation to variations in the perceived mass of the target as well as the presence or absence of gravity. Finally, short rest periods were interleaved within blocks of each task type to elucidate differences between active BCI intention and rest. Using a post-hoc state-decoder model, periods of active BCI task control could be distinguished from periods of rest with a very high degree of accuracy: ~99%). Taken together, the results from these experiments present a first step toward the design of a dynamics-based BCI system suitable for FNS applications as well as a framework for implementation of an asyncrhonous ECoG BCI