A causal perspective on brainwave modeling for brain-computer interfaces.

Objective. Machine learning (ML) models have opened up enormous opportunities in the field of brain-computer Interfaces (BCIs). Despite their great success, they usually face severe limitations when they are employed in real-life applications outside a controlled laboratory setting.Approach. Mixing causal reasoning, identifying causal relationships between variables of interest, with brainwave modeling can change one's viewpoint on some of these major challenges which can be found in various stages in the ML pipeline, ranging from data collection and data pre-processing to training methods and techniques.Main results. In this work, we employ causal reasoning and present a framework aiming to breakdown and analyze important challenges of brainwave modeling for BCIs.Significance. Furthermore, we present how general ML practices as well as brainwave-specific techniques can be utilized and solve some of these identified challenges. And finally, we discuss appropriate evaluation schemes in order to measure these techniques' performance and efficiently compare them with other methods that will be developed in the future.

Neurofeedback and epilepsy: Renaissance of an old self-regulation method?

Neurofeedback is a brain-computer interface tool enabling the user to self-regulate their neuronal activity, and ultimately, induce long-term brain plasticity, making it an interesting instrument to cure brain disorders. Although this method has been used successfully in the past as an adjunctive therapy in drug-resistant epilepsy, this approach remains under-explored and deserves more rigorous scientific inquiry. In this review, we present early neurofeedback protocols employed in epilepsy and provide a critical overview of the main clinical studies. We also describe the potential neurophysiological mechanisms through which neurofeedback may produce its therapeutic effects. Finally, we discuss how to innovate and standardize future neurofeedback clinical trials in epilepsy based on evidence from recent research studies.

The evolution of neuromodulation for chronic stroke: From neuroplasticity mechanisms to brain-computer interfaces.

Stroke is one of the most common and debilitating neurological conditions worldwide. Those who survive experience motor, sensory, speech, vision, and/or cognitive deficits that severely limit remaining quality of life. While rehabilitation programs can help improve patients' symptoms, recovery is often limited, and patients frequently continue to experience impairments in functional status. In this review, invasive neuromodulation techniques to augment the effects of conventional rehabilitation methods are described, including vagus nerve stimulation (VNS), deep brain stimulation (DBS) and brain-computer interfaces (BCIs). In addition, the evidence base for each of these techniques, pivotal trials, and future directions are explored. Finally, emerging technologies such as functional near-infrared spectroscopy (fNIRS) and the shift to artificial intelligence-enabled implants and wearables are examined. While the field of implantable devices for chronic stroke recovery is still in a nascent stage, the data reviewed are suggestive of immense potential for reducing the impact and impairment from this globally prevalent disorder.

Reducing calibration time in motor imagery-based BCIs by data alignment and empirical mode decomposition.

One of the major reasons that limit the practical applications of a brain-computer interface (BCI) is its long calibration time. In this paper, we propose a novel approach to reducing the calibration time of motor imagery (MI)-based BCIs without sacrificing classification accuracy. The approach aims to augment the training set size of a new subject by generating artificial electroencephalogram (EEG) data from a few training trials initially available. The artificial EEG data are obtained by first performing empirical mode decomposition (EMD) and then mixing resulting intrinsic mode functions (IMFs). The original training trials are aligned to common reference point with Euclidean alignment (EA) method prior to EMD and pooled together with artificial trials as the expended training set, which is input into a linear discriminant analysis (LDA) classifier or a logistic regression (LR) classifier. The performance of the proposed algorithm is evaluated on two motor imagery (MI) data sets and compared with that of the algorithm trained with only real EEG data (Baseline) and the algorithm trained with expanded EEG data by EMD without data alignment. The experimental results showed that the proposed algorithm can significantly reduce the amount of training data needed to achieve a given performance level and thus is expected to facilitate the real-world applications of MI-based BCIs.

Perinatal stroke: mapping and modulating developmental plasticity.

Most cases of hemiparetic cerebral palsy are caused by perinatal stroke, resulting in lifelong disability for millions of people. However, our understanding of how the motor system develops following such early unilateral brain injury is increasing. Tools such as neuroimaging and brain stimulation are generating informed maps of the unique motor networks that emerge following perinatal stroke. As a focal injury of defined timing in an otherwise healthy brain, perinatal stroke represents an ideal human model of developmental plasticity. Here, we provide an introduction to perinatal stroke epidemiology and outcomes, before reviewing models of developmental plasticity after perinatal stroke. We then examine existing therapeutic approaches, including constraint, bimanual and other occupational therapies, and their potential synergy with non-invasive neurostimulation. We end by discussing the promise of exciting new therapies, including novel neurostimulation, brain-computer interfaces and robotics, all focused on improving outcomes after perinatal stroke.

Non-invasive approaches to functional recovery after spinal cord injury: Therapeutic targets and multimodal device interventions.

This paper is an interdisciplinary narrative review of efficacious non-invasive therapies that are increasingly used to restore function in people with chronic spinal cord injuries (SCI). First presented are the secondary injury cascade set in motion by the primary lesion and highlights in therapeutic development for mitigating the acute pathophysiologic process. Then summarized are current pharmacological strategies for modulation of noradrenergic, serotonergic, and dopaminergic neurotransmission to enhance recovery in bench and clinical studies of subacute and chronic SCI. Last examined is how neuromechanical devices (i.e., electrical stimulation, robotic assistance, brain-computer interface, and augmented sensory feedback) could be comprehensively engineered to engage efferent and afferent motosensory pathways to induce neuroplasticity-based neural pattern generation. Emerging evidence shows that computational models of the human neuromusculoskeletal system (i.e., human digital twins) can serve as functionalized anchors to integrate different neuromechanical and pharmacological interventions into a single multimodal prothesis. The system, if appropriately built, may cybernetically optimize treatment outcomes via coordination of heterogeneous biosensory, system output, and control signals. Overall, these rehabilitation protocols involved neuromodulation to evoke beneficial adaptive changes within spared supraspinal, intracord, and peripheral neuromuscular circuits to elicit neurological improvement. Therefore, qualitatively advancing the theoretical understanding of spinal cord neurobiology and neuromechanics is pivotal to designing new ways to reinstate locomotion after SCI. Future research efforts should concentrate on personalizing combination therapies consisting of pharmacological adjuncts, targeted neurobiological and neuromuscular repairs, and brain-computer interfaces, which follow multimodal neuromechanical principles.

An EEG channel selection method for motor imagery based brain-computer interface and neurofeedback using Granger causality.

Motor imagery (MI) brain-computer interface (BCI) and neurofeedback (NF) with electroencephalogram (EEG) signals are commonly used for motor function improvement in healthy subjects and to restore neurological functions in stroke patients. Generally, in order to decrease noisy and redundant information in unrelated EEG channels, channel selection methods are used which provide feasible BCI and NF implementations with better performances. Our assumption is that there are causal interactions between the channels of EEG signal in MI tasks that are repeated in different trials of a BCI and NF experiment. Therefore, a novel method for EEG channel selection is proposed which is based on Granger causality (GC) analysis. Additionally, the machine-learning approach is used to cluster independent component analysis (ICA) components of the EEG signal into artifact and normal EEG clusters. After channel selection, using the common spatial pattern (CSP) and regularized CSP (RCSP), features are extracted and with the k-nearest neighbor (k-NN), support vector machine (SVM) and linear discriminant analysis (LDA) classifiers, MI tasks are classified into left and right hand MI. The goal of this study is to achieve a method resulting in lower EEG channels with higher classification performance in MI-based BCI and NF by causal constraint. The proposed method based on GC, with only eight selected channels, results in 93.03% accuracy, 92.93% sensitivity, and 93.12% specificity, with RCSP feature extractor and best classifier for each subject, after being applied on Physionet MI dataset, which is increased by 3.95%, 3.73%, and 4.13%, in comparison with correlation-based channel selection method.

Internal Feature Selection Method of CSP Based on L1-Norm and Dempster-Shafer Theory.

The common spatial pattern (CSP) algorithm is a well-recognized spatial filtering method for feature extraction in motor imagery (MI)-based brain-computer interfaces (BCIs). However, due to the influence of nonstationary in electroencephalography (EEG) and inherent defects of the CSP objective function, the spatial filters, and their corresponding features are not necessarily optimal in the feature space used within CSP. In this work, we design a new feature selection method to address this issue by selecting features based on an improved objective function. Especially, improvements are made in suppressing outliers and discovering features with larger interclass distances. Moreover, a fusion algorithm based on the Dempster-Shafer theory is proposed, which takes into consideration the distribution of features. With two competition data sets, we first evaluate the performance of the improved objective functions in terms of classification accuracy, feature distribution, and embeddability. Then, a comparison with other feature selection methods is carried out in both accuracy and computational time. Experimental results show that the proposed methods consume less additional computational cost and result in a significant increase in the performance of MI-based BCI systems.

Expressing uncertainty in Human-Robot interaction.

Most people struggle to understand probability which is an issue for Human-Robot Interaction (HRI) researchers who need to communicate risks and uncertainties to the participants in their studies, the media and policy makers. Previous work showed that even the use of numerical values to express probabilities does not guarantee an accurate understanding by laypeople. We therefore investigate if words can be used to communicate probability, such as "likely" and "almost certainly not". We embedded these phrases in the context of the usage of autonomous vehicles. The results show that the association of phrases to percentages is not random and there is a preferred order of phrases. The association is, however, not as consistent as hoped for. Hence, it would be advisable to complement the use of words with numerical expression of uncertainty. This study provides an empirically verified list of probabilities phrases that HRI researchers can use to complement the numerical values.

Cognitive Enhancement and Brain-Computer Interfaces: Potential Boundaries and Risks.

Electroencephalography (EEG) systems and brain-computer interfaces (BCIs) are terms frequently involved in the field of neurological research. Under a technological point of view, BCI is considered to be a significant achievement within the frame of learning disabilities rehabilitation. Nevertheless, the specifications for efficient use for cognitive enhancement and its potential boundaries are under concern. Author's main objective is to discuss BCI concrete components and potential advances as well as depict potential limitations while using technological devices within the frame of the learning procedure. Within this context, requirements, advantages, possible addiction risks, and boundaries regarding the specifications for brain-computer interfaces and technology in order to serve long-term research and developmental learning goals are discussed.

The future of upper extremity rehabilitation robotics: research and practice.

The loss of upper limb motor function can have a devastating effect on people's lives. To restore upper limb control and functionality, researchers and clinicians have developed interfaces to interact directly with the human body's motor system. In this invited review, we aim to provide details on the peripheral nerve interfaces and brain-machine interfaces that have been developed in the past 30 years for upper extremity control, and we highlight the challenges that still remain to transition the technology into the clinical market. The findings show that peripheral nerve interfaces and brain-machine interfaces have many similar characteristics that enable them to be concurrently developed. Decoding neural information from both interfaces may lead to novel physiological models that may one day fully restore upper limb motor function for a growing patient population.

Speech-related dorsal motor cortex activity does not interfere with iBCI cursor control.

OBJECTIVE: Speech-related neural modulation was recently reported in 'arm/hand' area of human dorsal motor cortex that is used as a signal source for intracortical brain-computer interfaces (iBCIs). This raises the concern that speech-related modulation might deleteriously affect the decoding of arm movement intentions, for instance by affecting velocity command outputs. This study sought to clarify whether or not speaking would interfere with ongoing iBCI use. APPROACH: A participant in the BrainGate2 iBCI clinical trial used an iBCI to control a computer cursor; spoke short words in a stand-alone speech task; and spoke short words during ongoing iBCI use. We examined neural activity in all three behaviors and compared iBCI performance with and without concurrent speech. MAIN RESULTS: Dorsal motor cortex firing rates modulated strongly during stand-alone speech, but this activity was largely attenuated when speaking occurred during iBCI cursor control using attempted arm movements. 'Decoder-potent' projections of the attenuated speech-related neural activity were small, explaining why cursor task performance was similar between iBCI use with and without concurrent speaking. SIGNIFICANCE: These findings indicate that speaking does not directly interfere with iBCIs that decode attempted arm movements. This suggests that patients who are able to speak will be able to use motor cortical-driven computer interfaces or prostheses without needing to forgo speaking while using these devices.

Brain-computer interfaces for amyotrophic lateral sclerosis.

A brain-computer interface (BCI) is a device that detects signals from the brain and transforms them into useful commands. Researchers have developed BCIs that utilize different kinds of brain signals. These different BCI systems have differing characteristics, such as the amount of training required and the degree to which they are or are not invasive. Much of the research on BCIs to date has involved healthy individuals and evaluation of classification algorithms. Some BCIs have been shown to have potential benefit for users with minimal muscular function as a result of amyotrophic lateral sclerosis. However, there are still several challenges that need to be successfully addressed before BCIs can be clinically useful.

Sensor Modalities for Brain-Computer Interface Technology: A Comprehensive Literature Review.

Brain-computer interface (BCI) technology is rapidly developing and changing the paradigm of neurorestoration by linking cortical activity with control of an external effector to provide patients with tangible improvements in their ability to interact with the environment. The sensor component of a BCI circuit dictates the resolution of brain pattern recognition and therefore plays an integral role in the technology. Several sensor modalities are currently in use for BCI applications and are broadly either electrode-based or functional neuroimaging-based. Sensors vary in their inherent spatial and temporal resolutions, as well as in practical aspects such as invasiveness, portability, and maintenance. Hybrid BCI systems with multimodal sensory inputs represent a promising development in the field allowing for complimentary function. Artificial intelligence and deep learning algorithms have been applied to BCI systems to achieve faster and more accurate classifications of sensory input and improve user performance in various tasks. Neurofeedback is an important advancement in the field that has been implemented in several types of BCI systems by showing users a real-time display of their recorded brain activity during a task to facilitate their control over their own cortical activity. In this way, neurofeedback has improved BCI classification and enhanced user control over BCI output. Taken together, BCI systems have progressed significantly in recent years in terms of accuracy, speed, and communication. Understanding the sensory components of a BCI is essential for neurosurgeons and clinicians as they help advance this technology in the clinical setting.