Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework.

Interictal Epileptiform Discharges (IED) and High Frequency Oscillations (HFO) in intraoperative electrocorticography (ECoG) may guide the surgeon by delineating the epileptogenic zone. We designed a modular spiking neural network (SNN) in a mixed-signal neuromorphic device to process the ECoG in real-time. We exploit the variability of the inhomogeneous silicon neurons to achieve efficient sparse and decorrelated temporal signal encoding. We interface the full-custom SNN device to the BCI2000 real-time framework and configure the setup to detect HFO and IED co-occurring with HFO (IED-HFO). We validate the setup on pre-recorded data and obtain HFO rates that are concordant with a previously validated offline algorithm (Spearman's ρ = 0.75, p = 1e-4), achieving the same postsurgical seizure freedom predictions for all patients. In a remote on-line analysis, intraoperative ECoG recorded in Utrecht was compressed and transferred to Zurich for SNN processing and successful IED-HFO detection in real-time. These results further demonstrate how automated remote real-time detection may enable the use of HFO in clinical practice.

Using fMRI to localize target regions for implanted brain-computer interfaces in locked-in syndrome.

OBJECTIVE: Electrocorticography (ECoG)-based brain-computer interface (BCI) systems have the potential to improve quality of life of people with locked-in syndrome (LIS) by restoring their ability to communicate independently. Before implantation of such a system, it is important to localize ECoG electrode target regions. Here, we assessed the predictive value of functional magnetic resonance imaging (fMRI) for the localization of suitable target regions on the sensorimotor cortex for ECoG-based BCI in people with locked-in syndrome. METHODS: Three people with locked-in syndrome were implanted with a chronic, fully implantable ECoG-BCI system. We compared pre-surgical fMRI activity with post-implantation ECoG activity from areas known to be active and inactive during attempted hand movement (sensorimotor hand region and dorsolateral prefrontal cortex, respectively). RESULTS: Results showed a spatial match between fMRI activity and changes in ECoG low and high frequency band power (10 - 30 and 65 - 95 Hz, respectively) during attempted movement. Also, we found that fMRI can be used to select a sub-set of electrodes that show strong task-related signal changes that are therefore likely to generate adequate BCI control. CONCLUSIONS: Our findings indicate that fMRI is a useful non-invasive tool for the pre-surgical workup of BCI implant candidates. SIGNIFICANCE: If these results are confirmed in more BCI studies, fMRI might be used for more efficient surgical BCI procedures with focused cortical coverage and lower participant burden.

Direct speech reconstruction from sensorimotor brain activity with optimized deep learning models.

Objective.Development of brain-computer interface (BCI) technology is key for enabling communication in individuals who have lost the faculty of speech due to severe motor paralysis. A BCI control strategy that is gaining attention employs speech decoding from neural data. Recent studies have shown that a combination of direct neural recordings and advanced computational models can provide promising results. Understanding which decoding strategies deliver best and directly applicable results is crucial for advancing the field.Approach.In this paper, we optimized and validated a decoding approach based on speech reconstruction directly from high-density electrocorticography recordings from sensorimotor cortex during a speech production task.Main results.We show that (1) dedicated machine learning optimization of reconstruction models is key for achieving the best reconstruction performance; (2) individual word decoding in reconstructed speech achieves 92%-100% accuracy (chance level is 8%); (3) direct reconstruction from sensorimotor brain activity produces intelligible speech.Significance.These results underline the need for model optimization in achieving best speech decoding results and highlight the potential that reconstruction-based speech decoding from sensorimotor cortex can offer for development of next-generation BCI technology for communication.

Nine decades of electrocorticography: A comparison between epidural and subdural recordings.

In recent years, electrocorticography (ECoG) has arisen as a neural signal recording tool in the development of clinically viable neural interfaces. ECoG electrodes are generally placed below the dura mater (subdural) but can also be placed on top of the dura (epidural). In deciding which of these modalities best suits long-term implants, complications and signal quality are important considerations. Conceptually, epidural placement may present a lower risk of complications as the dura is left intact but also a lower signal quality due to the dura acting as a signal attenuator. The extent to which complications and signal quality are affected by the dura, however, has been a matter of debate. To improve our understanding of the effects of the dura on complications and signal quality, we conducted a literature review. We inventorized the effect of the dura on signal quality, decodability and longevity of acute and chronic ECoG recordings in humans and non-human primates. Also, we compared the incidence and nature of serious complications in studies that employed epidural and subdural ECoG. Overall, we found that, even though epidural recordings exhibit attenuated signal amplitude over subdural recordings, particularly for high-density grids, the decodability of epidural recorded signals does not seem to be markedly affected. Additionally, we found that the nature of serious complications was comparable between epidural and subdural recordings. These results indicate that both epidural and subdural ECoG may be suited for long-term neural signal recordings, at least for current generations of clinical and high-density ECoG grids.

Methodological Recommendations for Studies on the Daily Life Implementation of Implantable Communication-Brain-Computer Interfaces for Individuals With Locked-in Syndrome.

Implantable brain-computer interfaces (BCIs) promise to be a viable means to restore communication in individuals with locked-in syndrome (LIS). In 2016, we presented the world-first fully implantable BCI system that uses subdural electrocorticography electrodes to record brain signals and a subcutaneous amplifier to transmit the signals to the outside world, and that enabled an individual with LIS to communicate via a tablet computer by selecting icons in spelling software. For future clinical implementation of implantable communication-BCIs, however, much work is still needed, for example, to validate these systems in daily life settings with more participants, and to improve the speed of communication. We believe the design and execution of future studies on these and other topics may benefit from the experience we have gained. Therefore, based on relevant literature and our own experiences, we here provide an overview of procedures, as well as recommendations, for recruitment, screening, inclusion, imaging, hospital admission, implantation, training, and support of participants with LIS, for studies on daily life implementation of implantable communication-BCIs. With this article, we not only aim to inform the BCI community about important topics of concern, but also hope to contribute to improved methodological standardization of implantable BCI research.

Towards predicting ECoG-BCI performance: assessing the potential of scalp-EEG.

Objective. Implanted brain-computer interfaces (BCIs) employ neural signals to control a computer and may offer an alternative communication channel for people with locked-in syndrome (LIS). Promising results have been obtained using signals from the sensorimotor (SM) area. However, in earlier work on home-use of an electrocorticography (ECoG)-based BCI by people with LIS, we detected differences in ECoG-BCI performance, which were related to differences in the modulation of low frequency band (LFB) power in the SM area. For future clinical implementation of ECoG-BCIs, it will be crucial to determine whether reliable performance can be predicted before electrode implantation. To assess if non-invasive scalp-electroencephalography (EEG) could serve such prediction, we here investigated if EEG can detect the characteristics observed in the LFB modulation of ECoG signals.Approach. We included three participants with LIS of the earlier study, and a control group of 20 healthy participants. All participants performed a Rest task, and a Movement task involving actual (healthy) or attempted (LIS) hand movements, while their EEG signals were recorded.Main results.Data of the Rest task was used to determine signal-to-noise ratio, which showed a similar range for LIS and healthy participants. Using data of the Movement task, we selected seven EEG electrodes that showed a consistent movement-related decrease in beta power (13-30 Hz) across healthy participants. Within the EEG recordings of this subset of electrodes of two LIS participants, we recognized the phenomena reported earlier for the LFB in their ECoG recordings. Specifically, strong movement-related beta band suppression was observed in one, but not the other, LIS participant, and movement-related alpha band (8-12 Hz) suppression was practically absent in both. Results of the third LIS participant were inconclusive due to technical issues with the EEG recordings.Significance. Together, these findings support a potential role for scalp EEG in the presurgical assessment of ECoG-BCI candidates.

Open multimodal iEEG-fMRI dataset from naturalistic stimulation with a short audiovisual film.

Intracranial human recordings are a valuable and rare resource of information about the brain. Making such data publicly available not only helps tackle reproducibility issues in science, it helps make more use of these valuable data. This is especially true for data collected using naturalistic tasks. Here, we describe a dataset collected from a large group of human subjects while they watched a short audiovisual film. The dataset has several unique features. First, it includes a large amount of intracranial electroencephalography (iEEG) data (51 participants, age range of 5-55 years, who all performed the same task). Second, it includes functional magnetic resonance imaging (fMRI) recordings (30 participants, age range of 7-47) during the same task. Eighteen participants performed both iEEG and fMRI versions of the task, non-simultaneously. Third, the data were acquired using a rich audiovisual stimulus, for which we provide detailed speech and video annotations. This dataset can be used to study neural mechanisms of multimodal perception and language comprehension, and similarity of neural signals across brain recording modalities.

FMRI and intra-cranial electrocorticography recordings in the same human subjects reveals negative BOLD signal coupled with silenced neuronal activity.

Positive blood oxygenation level-dependent (BOLD) responses (PBR), as measured by functional Magnetic Resonance Imaging (fMRI), are the most utilized measurements to non-invasively map activity in the brain. Recent studies have consistently shown that BOLD responses are not exclusively positive. Negative BOLD responses (NBR) have been reported in response to specific sensory stimulations and tasks. However, the exact relationship between NBR and the underlying metabolic and neuronal demand is still under debate. In this study, we investigated the neurophysiological basis of negative BOLD using fMRI and intra-cranial electrophysiology (electrocorticography, ECoG) measurements from the same human participants. We show that, for those electrodes that responded to visual stimulation, PBR are correlated with high-frequency band (HFB) responses. Crucially, NBR were associated with an absence of HFB power responses and an unpredicted decrease in the alpha power responses.

Decoding four hand gestures with a single bipolar pair of electrocorticography electrodes.

Objective.Electrocorticography (ECoG) based brain-computer interfaces (BCIs) can be used to restore communication in individuals with locked-in syndrome. In motor-based BCIs, the number of degrees-of-freedom, and thus the speed of the BCI, directly depends on the number of classes that can be discriminated from the neural activity in the sensorimotor cortex. When considering minimally invasive BCI implants, the size of the subdural ECoG implant must be minimized without compromising the number of degrees-of-freedom.Approach.Here we investigated if four hand gestures could be decoded using a single ECoG strip of four consecutive electrodes spaced 1 cm apart and compared the performance between a unipolar and a bipolar montage. For that we collected data of seven individuals with intractable epilepsy implanted with ECoG grids, covering the hand region of the sensorimotor cortex. Based on the implanted grids, we generated virtual ECoG strips and compared the decoding accuracy between (a) a single unipolar electrode (Unipolar Electrode), (b) a combination of four unipolar electrodes (Unipolar Strip), (c) a single bipolar pair (Bipolar Pair) and (d) a combination of six bipolar pairs (Bipolar Strip).Main results.We show that four hand gestures can be equally well decoded using 'Unipolar Strips' (mean 67.4 ± 11.7%), 'Bipolar Strips' (mean 66.6 ± 12.1%) and 'Bipolar Pairs' (mean 67.6 ± 9.4%), while 'Unipolar Electrodes' (61.6 ± 5.9%) performed significantly worse compared to 'Unipolar Strips' and 'Bipolar Pairs'.Significance.We conclude that a single bipolar pair is a potential candidate for minimally invasive motor-based BCIs and encourage the use of ECoG as a robust and reliable BCI platform for multi-class movement decoding.

Brain-Computer Interfaces for Communication: Preferences of Individuals With Locked-in Syndrome.

BACKGROUND: Brain-computer interfaces (BCIs) have been proposed as an assistive technology (AT) allowing people with locked-in syndrome (LIS) to use neural signals to communicate. To design a communication BCI (cBCI) that is fully accepted by the users, their opinion should be taken into consideration during the research and development process. OBJECTIVE: We assessed the preferences of prospective cBCI users regarding (1) the applications they would like to control with a cBCI, (2) the mental strategies they would prefer to use to control the cBCI, and (3) when during their clinical trajectory they would like to be informed about AT and cBCIs. Furthermore, we investigated if individuals diagnosed with progressive and sudden onset (SO) disorders differ in their opinion. METHODS: We interviewed 28 Dutch individuals with LIS during a 3-hour home visit using multiple-choice, ranking, and open questions. During the interview, participants were informed about BCIs and the possible mental strategies. RESULTS: Participants rated (in)direct forms of communication, computer use, and environmental control as the most desired cBCI applications. In addition, active cBCI control strategies were preferred over reactive strategies. Furthermore, individuals with progressive and SO disorders preferred to be informed about AT and cBCIs at the moment they would need it. CONCLUSIONS: We show that individuals diagnosed with progressive and SO disorders preferred, in general, the same applications, mental strategies, and time of information. By collecting the opinion of a large sample of individuals with LIS, this study provides valuable information to stakeholders in cBCI and other AT development.

Dorsolateral prefrontal cortex-based control with an implanted brain-computer interface.

The objective of this study was to test the feasibility of using the dorsolateral prefrontal cortex as a signal source for brain-computer interface control in people with severe motor impairment. We implanted two individuals with locked-in syndrome with a chronic brain-computer interface designed to restore independent communication. The implanted system (Utrecht NeuroProsthesis) included electrode strips placed subdurally over the dorsolateral prefrontal cortex. In both participants, counting backwards activated the dorsolateral prefrontal cortex consistently over the course of 47 and 22 months, respectively. Moreover, both participants were able to use this signal to control a cursor in one dimension, with average accuracy scores of 78 ± 9% (standard deviation) and 71 ± 11% (chance level: 50%), respectively. Brain-computer interface control based on dorsolateral prefrontal cortex activity is feasible in people with locked-in syndrome and may become of relevance for those unable to use sensorimotor signals for control.

Sensorimotor ECoG Signal Features for BCI Control: A Comparison Between People With Locked-In Syndrome and Able-Bodied Controls.

The sensorimotor cortex is a frequently targeted brain area for the development of Brain-Computer Interfaces (BCIs) for communication in people with severe paralysis and communication problems (locked-in syndrome; LIS). It is widely acknowledged that this area displays an increase in high-frequency band (HFB) power and a decrease in the power of the low frequency band (LFB) during movement of, for example, the hand. Upon termination of hand movement, activity in the LFB band typically shows a short increase (rebound). The ability to modulate the neural signal in the sensorimotor cortex by imagining or attempting to move is crucial for the implementation of sensorimotor BCI in people who are unable to execute movements. This may not always be self-evident, since the most common causes of LIS, amyotrophic lateral sclerosis (ALS) and brain stem stroke, are associated with significant damage to the brain, potentially affecting the generation of baseline neural activity in the sensorimotor cortex and the modulation thereof by imagined or attempted hand movement. In the Utrecht NeuroProsthesis (UNP) study, a participant with LIS caused by ALS and a participant with LIS due to brain stem stroke were implanted with a fully implantable BCI, including subdural electrocorticography (ECoG) electrodes over the sensorimotor area, with the purpose of achieving ECoG-BCI-based communication. We noted differences between these participants in the spectral power changes generated by attempted movement of the hand. To better understand the nature and origin of these differences, we compared the baseline spectral features and task-induced modulation of the neural signal of the LIS participants, with those of a group of able-bodied people with epilepsy who received a subchronic implant with ECoG electrodes for diagnostic purposes. Our data show that baseline LFB oscillatory components and changes generated in the LFB power of the sensorimotor cortex by (attempted) hand movement differ between participants, despite consistent HFB responses in this area. We conclude that the etiology of LIS may have significant effects on the LFB spectral components in the sensorimotor cortex, which is relevant for the development of communication-BCIs for this population.

Stability of a chronic implanted brain-computer interface in late-stage amyotrophic lateral sclerosis.

OBJECTIVE: We investigated the long-term functional stability and home use of a fully implanted electrocorticography (ECoG)-based brain-computer interface (BCI) for communication by an individual with late-stage Amyotrophic Lateral Sclerosis (ALS). METHODS: Data recorded from the cortical surface of the motor and prefrontal cortex with an implanted brain-computer interface device was evaluated for 36 months after implantation of the system in an individual with late-stage ALS. In addition, electrode impedance and BCI control accuracy were assessed. Key measures included frequency of use of the system for communication, user and system performance, and electrical signal characteristics. RESULTS: User performance was high consistently over the three years. Power in the high frequency band, used for the control signal, declined slowly in the motor cortex, but control over the signal remained unaffected by time. Impedance increased until month 5, and then remained constant. Frequency of home use increased steadily, indicating adoption of the system by the user. CONCLUSIONS: The implanted brain-computer interface proves to be robust in an individual with late-stage ALS, given stable performance and control signal for over 36 months. SIGNIFICANCE: These findings are relevant for the future of implantable brain-computer interfaces along with other brain-sensing technologies, such as responsive neurostimulation.

High-frequency band temporal dynamics in response to a grasp force task.

OBJECTIVE: Brain-computer interfaces (BCIs) are being developed to restore reach and grasping movements of paralyzed individuals. Recent studies have shown that the kinetics of grasping movement, such as grasp force, can be successfully decoded from electrocorticography (ECoG) signals, and that the high-frequency band (HFB) power changes provide discriminative information that contribute to an accurate decoding of grasp force profiles. However, as the models used in these studies contained simultaneous information from multiple spectral features over multiple areas in the brain, it remains unclear what parameters of movement and force are encoded by the HFB signals and how these are represented temporally and spatially in the SMC. APPROACH: To investigate this, and to gain insight in the temporal dynamics of the HFB during grasping, we continuously modelled the ECoG HFB response recorded from nine individuals with epilepsy temporarily implanted with ECoG grids, who performed three different grasp force tasks. MAIN RESULTS: We show that a model based on the force onset and offset consistently provides a better fit to the HFB power responses when compared with a model based on the force magnitude, irrespective of electrode location. SIGNIFICANCE: Our results suggest that HFB power, although potentially useful for continuous decoding, is more closely related to the changes in movement. This finding may potentially contribute to the more natural decoding of grasping movement in neural prosthetics.

Optimization of sampling rate and smoothing improves classification of high frequency power in electrocorticographic brain signals.

OBJECTIVE: High-frequency band (HFB) activity, measured using implanted sensors over the cortex, is increasingly considered as a feature for the study of brain function and the design of neural-implants, such as Brain-Computer Interfaces (BCIs). One common way of extracting these power signals is using a wavelet dictionary, which involves the selection of different temporal sampling and temporal smoothing parameters, such that the resulting HFB signal best represents the temporal features of the neuronal event of interest. Typically, the use of neuro-electrical signals for closed-loop BCI control requires a certain level of signal downsampling and smoothing in order to remove uncorrelated noise, optimize performance and provide fast feedback. However, a fixed setting of the sampling and smoothing parameters may lead to a suboptimal representation of the underlying neural responses and poor BCI control. This problem can be resolved with a systematic assessment of parameter settings. APPROACH: With classification of HFB power responses as performance measure, different combinations of temporal sampling and temporal smoothing values were applied to data from sensory and motor tasks recorded with high-density and standard clinical electrocorticography (ECoG) grids in 12 epilepsy patients. MAIN RESULTS: The results suggest that HFB ECoG responses are best performed with high sampling and subsequent smoothing. For the paradigms used in this study, optimal temporal sampling ranged from 29 Hz to 50 Hz. Regarding optimal smoothing, values were similar between tasks (0.1-0.9 s), except for executed complex hand gestures, for which two optimal possible smoothing windows were found (0.4-0.6 s and 0.9-2.7 s). SIGNIFICANCE: The range of optimal values indicates that parameter optimization depends on the functional paradigm and may be subject-specific. Our results advocate a methodical assessment of parameter settings for optimal decodability of ECoG signals.

Workshops of the Sixth International Brain-Computer Interface Meeting: brain-computer interfaces past, present, and future.

The Sixth International Brain-Computer Interface (BCI) Meeting was held 30 May-3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain-machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the general public. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and highlighting important issues and calls for action to support future research and development.

Estimated Prevalence of the Target Population for Brain-Computer Interface Neurotechnology in the Netherlands.

BACKGROUND: People who suffer from paralysis have difficulties participating in society. Particularly burdensome is the locked-in syndrome (LIS). LIS patients are not able to move and speak but are cognitively healthy. They rely on assistive technology to interact with the world and may benefit from neurotechnological advances. Optimal research and design of such aids requires a well-defined target population. However, the LIS population is poorly characterized and the number of patients in this condition is unknown. OBJECTIVE: Here we estimated and described the LIS patient population in the Netherlands to define the target population for assistive (neuro)technology. METHODS: We asked physicians in the Netherlands if they had patients suffering from severe paralysis and communication problems in their files. Physicians responding affirmatively were asked to fill out a questionnaire on the patients' status. RESULTS: We sent out 9570 letters to general practitioners (GPs), who reported 83 patients. After first screening, the GPs of 46 patients received the questionnaire. Based on the responses, 26 patients were classified as having LIS. Extrapolation of these numbers resulted in a prevalence of 0.73 patients per 100 000 inhabitants. Notable results from the questionnaire were the percentage of patients with neuromuscular disease (>50%) and living at home (>70%). CONCLUSIONS: We revealed an etiologically diverse group of LIS patients. The functioning and needs of these patients were, however, similar and many relied on assistive technology. By characterizing the LIS population, our study may contribute to optimal development of assistive (neuro)technology.

Decoding hand gestures from primary somatosensory cortex using high-density ECoG.

Electrocorticography (ECoG) based Brain-Computer Interfaces (BCIs) have been proposed as a way to restore and replace motor function or communication in severely paralyzed people. To date, most motor-based BCIs have either focused on the sensorimotor cortex as a whole or on the primary motor cortex (M1) as a source of signals for this purpose. Still, target areas for BCI are not confined to M1, and more brain regions may provide suitable BCI control signals. A logical candidate is the primary somatosensory cortex (S1), which not only shares similar somatotopic organization to M1, but also has been suggested to have a role beyond sensory feedback during movement execution. Here, we investigated whether four complex hand gestures, taken from the American sign language alphabet, can be decoded exclusively from S1 using both spatial and temporal information. For decoding, we used the signal recorded from a small patch of cortex with subdural high-density (HD) grids in five patients with intractable epilepsy. Notably, we introduce a new method of trial alignment based on the increase of the electrophysiological response, which virtually eliminates the confounding effects of systematic and non-systematic temporal differences within and between gestures execution. Results show that S1 classification scores are high (76%), similar to those obtained from M1 (74%) and sensorimotor cortex as a whole (85%), and significantly above chance level (25%). We conclude that S1 offers characteristic spatiotemporal neuronal activation patterns that are discriminative between gestures, and that it is possible to decode gestures with high accuracy from a very small patch of cortex using subdurally implanted HD grids. The feasibility of decoding hand gestures using HD-ECoG grids encourages further investigation of implantable BCI systems for direct interaction between the brain and external devices with multiple degrees of freedom.

Fully Implanted Brain-Computer Interface in a Locked-In Patient with ALS.

Options for people with severe paralysis who have lost the ability to communicate orally are limited. We describe a method for communication in a patient with late-stage amyotrophic lateral sclerosis (ALS), involving a fully implanted brain-computer interface that consists of subdural electrodes placed over the motor cortex and a transmitter placed subcutaneously in the left side of the thorax. By attempting to move the hand on the side opposite the implanted electrodes, the patient accurately and independently controlled a computer typing program 28 weeks after electrode placement, at the equivalent of two letters per minute. The brain-computer interface offered autonomous communication that supplemented and at times supplanted the patient's eye-tracking device. (Funded by the Government of the Netherlands and the European Union; ClinicalTrials.gov number, NCT02224469 .).