Automatic processing of self-regulation of slow cortical potentials: evidence from brain-computer communication in paralysed patients

How standardized are “standard protocols”? Variations in protocol and performance evaluation for slow cortical potential neurofeedback: A systematic review

Neurofeedback (NF) aims to alter neural activity by enhancing self-regulation skills. Over the past decade NF has received considerable attention as a potential intervention option for many somatic and mental conditions and ADHD in particular. However, placebo-controlled trials have demonstrated insufficient superiority of NF compared to treatment as usual and sham conditions. It has been argued that the reason for limited NF effects may be attributable to participants' challenges to self-regulate the targeted neural activity. Still, there is support of NF efficacy when only considering so-called “standard protocols,” such as Slow Cortical Potential NF training (SCP-NF). This PROSPERO registered systematic review following PRISMA criteria searched literature databases for studies applying SCP-NF protocols. Our review focus concerned the operationalization of self-regulatory success, and protocol-details that could influence the evaluation of self-regulation. Such details included; electrode placement, number of trials, length per trial, proportions of training modalities, handling of artifacts and skill-transfer into daily-life. We identified a total of 63 eligible reports published in the year 2000 or later. SCP-NF protocol-details varied considerably on most variables, except for electrode placement. However, due to the increased availability of commercial systems, there was a trend to more uniform protocol-details. Although, token-systems are popular in SCP-NF for ADHD, only half reported a performance-based component. Also, transfer exercises have become a staple part of SCP-NF. Furthermore, multiple operationalizations of regulatory success were identified, limiting comparability between studies, and perhaps usefulness of so-called transfer-exercises, which purpose is to facilitate the transfer of the self-regulatory skills into every-day life. While studies utilizing SCP as Brain-Computer-Interface mainly focused on the acquisition of successful self-regulation, clinically oriented studies often neglected this. Congruently, rates of successful regulators in clinical studies were mostly low (<50%). The relation between SCP self-regulation and behavior, and how symptoms in different disorders are affected, is complex and not fully understood. Future studies need to report self-regulation based on standardized measures, in order to facilitate both comparability and understanding of the effects on symptoms. When applied as treatment, future SCP-NF studies also need to put greater emphasis on the acquisition of self-regulation (before evaluating symptom outcomes).

Participant-specific classifier tuning increases the performance of hand movement detection from EEG in patients with amyotrophic lateral sclerosis

Objective.Brain-computer interface (BCI) systems can be employed to provide motor and communication assistance to patients suffering from neuromuscular diseases, such as amyotrophic lateral sclerosis (ALS). Movement related cortical potentials (MRCPs), which are naturally generated during movement execution, can be used to implement a BCI triggered by motor attempts. Such BCI could assist impaired motor functions of ALS patients during disease progression, and facilitate the training for the generation of reliable MRCPs. The training aspect is relevant to establish a communication channel in the late stage of the disease. Therefore, the aim of this study was to investigate the possibility of detecting MRCPs associated to movement intention in ALS patients with different levels of disease progression from slight to complete paralysis.Approach.Electroencephalography signals were recorded from nine channels in 30 ALS patients at various stages of the disease while they performed or attempted to perform hand movements timed to a visual cue. The movement detection was implemented using offline classification between movement and rest phase. Temporal and spectral features were extracted using 500 ms sliding windows with 50% overlap. The detection was tested for each individual channel and two surrogate channels by performing feature selection followed by classification using linear and non-linear support vector machine and linear discriminant analysis.Main results.The results demonstrated that the detection performance was high in all patients (accuracy 80.5 ± 5.6%) but that the classification parameters (channel, features and classifier) leading to the best performance varied greatly across patients. When the same channel and classifier were used for all patients (participant-generic analysis), the performance significantly decreased (accuracy 74 ± 8.3%).Significance.The present study demonstrates that to maximize the detection of brain waves across ALS patients at different stages of the disease, the classification pipeline should be tuned to each patient individually.

Suppress Me if You Can: Neurofeedback of the Readiness Potential

Voluntary movements are usually preceded by a slow, negative-going brain signal over motor areas, the so-called readiness potential (RP). To date, the exact nature and causal role of the RP in movement preparation have remained heavily debated. Although the RP is influenced by several motorical and cognitive factors, it has remained unclear whether people can learn to exert mental control over their RP, for example, by deliberately suppressing it. If people were able to initiate spontaneous movements without eliciting an RP, this would challenge the idea that the RP is a necessary stage of the causal chain leading up to a voluntary movement. We tested the ability of participants to control the magnitude of their RP in a neurofeedback experiment. Participants performed self-initiated movements, and after every movement, they were provided with immediate feedback about the magnitude of their RP. They were asked to find a strategy to perform voluntary movements such that the RPs were as small as possible. We found no evidence that participants were able to to willfully modulate or suppress their RPs while still eliciting voluntary movements. This suggests that the RP might be an involuntary component of voluntary action over which people cannot exert conscious control.

Regularized Group Sparse Discriminant Analysis for P300-Based Brain–Computer Interface

Event-related potentials (ERPs) especially P300 are popular effective features for brain–computer interface (BCI) systems based on electroencephalography (EEG). Traditional ERP-based BCI systems may perform poorly for small training samples, i.e. the undersampling problem. In this study, the ERP classification problem was investigated, in particular, the ERP classification in the high-dimensional setting with the number of features larger than the number of samples was studied. A flexible group sparse discriminative analysis algorithm based on Moreau–Yosida regularization was proposed for alleviating the undersampling problem. An optimization problem with the group sparse criterion was presented, and the optimal solution was proposed by using the regularized optimal scoring method. During the alternating iteration procedure, the feature selection and classification were performed simultaneously. Two P300-based BCI datasets were used to evaluate our proposed new method and compare it with existing standard methods. The experimental results indicated that the features extracted via our proposed method are efficient and provide an overall better P300 classification accuracy compared with several state-of-the-art methods.

A 20-Questions-Based Binary Spelling Interface for Communication Systems

Brain computer interfaces (BCIs) enables people with motor impairments to communicate using their brain signals by selecting letters and words from a screen. However, these spellers do not work for people in a complete locked-in state (CLIS). For these patients, a near infrared spectroscopy-based BCI has been developed, allowing them to reply to “yes”/”no” questions with a classification accuracy of 70%. Because of the non-optimal accuracy, a usual character-based speller for selecting letters or words cannot be used. In this paper, a novel spelling interface based on the popular 20-questions-game has been presented, which will allow patients to communicate using only “yes”/”no” answers, even in the presence of poor classification accuracy. The communication system is based on an artificial neural network (ANN) that estimates a statement thought by the patient asking less than 20 questions. The ANN has been tested in a web-based version with healthy participants and in offline simulations. Both results indicate that the proposed system can estimate a patient’s imagined sentence with an accuracy that varies from 40%, in the case of a “yes”/”no” classification accuracy of 70%, and up to 100% in the best case. These results show that the proposed spelling interface could allow patients in CLIS to express their own thoughts, instead of only answer to “yes”/”no” questions.

Brain-Computer Interface-Based Communication in the Completely Locked-In State

Despite partial success, communication has remained impossible for persons suffering from complete motor paralysis but intact cognitive and emotional processing, a state called complete locked-in state (CLIS). Based on a motor learning theoretical context and on the failure of neuroelectric brain–computer interface (BCI) communication attempts in CLIS, we here report BCI communication using functional near-infrared spectroscopy (fNIRS) and an implicit attentional processing procedure. Four patients suffering from advanced amyotrophic lateral sclerosis (ALS)—two of them in permanent CLIS and two entering the CLIS without reliable means of communication—learned to answer personal questions with known answers and open questions all requiring a “yes” or “no” thought using frontocentral oxygenation changes measured with fNIRS. Three patients completed more than 46 sessions spread over several weeks, and one patient (patient W) completed 20 sessions. Online fNIRS classification of personal questions with known answers and open questions using linear support vector machine (SVM) resulted in an above-chance-level correct response rate over 70%. Electroencephalographic oscillations and electrooculographic signals did not exceed the chance-level threshold for correct communication despite occasional differences between the physiological signals representing a “yes” or “no” response. However, electroencephalogram (EEG) changes in the theta-frequency band correlated with inferior communication performance, probably because of decreased vigilance and attention. If replicated with ALS patients in CLIS, these positive results could indicate the first step towards abolition of complete locked-in states, at least for ALS.

"Locked in" patients suffering from advanced amyotrophic lateral sclerosis, with no reliable means of communication, can learn to answer questions requiring a “yes” or “no” thought using frontocentral oxygenation changes measurable by functional near-infrared spectroscopy.

Despite scientific and technological advances, communication has remained impossible for persons suffering from complete motor paralysis but intact cognitive and emotional processing, a condition that is called completely locked-in state. Brain–computer interfaces based on neuroelectrical technology (like an electroencephalogram) have failed at providing patients in a completely locked-in state with means to communicate. Therefore, here we explored if a brain–computer interface based on functional near infrared spectroscopy (fNIRS)—which measures brain hemodynamic responses associated with neuronal activity—could overcome this barrier. Four patients suffering from advanced amyotrophic lateral sclerosis (ALS), two of them in permanent completely locked-in state and two entering the completely locked-in state without reliable means of communication, learned to answer personal questions with known answers and open questions requiring a “yes” or “no” by using frontocentral oxygenation changes measured with fNIRS. These results are, potentially, the first step towards abolition of completely locked-in states, at least for patients with ALS.

EEG Negativity in Fixations Used for Gaze-Based Control: Toward Converting Intentions into Actions with an Eye-Brain-Computer Interface

We usually look at an object when we are going to manipulate it. Thus, eye tracking can be used to communicate intended actions. An effective human-machine interface, however, should be able to differentiate intentional and spontaneous eye movements. We report an electroencephalogram (EEG) marker that differentiates gaze fixations used for control from spontaneous fixations involved in visual exploration. Eight healthy participants played a game with their eye movements only. Their gaze-synchronized EEG data (fixation-related potentials, FRPs) were collected during game's control-on and control-off conditions. A slow negative wave with a maximum in the parietooccipital region was present in each participant's averaged FRPs in the control-on conditions and was absent or had much lower amplitude in the control-off condition. This wave was similar but not identical to stimulus-preceding negativity, a slow negative wave that can be observed during feedback expectation. Classification of intentional vs. spontaneous fixations was based on amplitude features from 13 EEG channels using 300 ms length segments free from electrooculogram contamination (200-500 ms relative to the fixation onset). For the first fixations in the fixation triplets required to make moves in the game, classified against control-off data, a committee of greedy classifiers provided 0.90 ± 0.07 specificity and 0.38 ± 0.14 sensitivity. Similar (slightly lower) results were obtained for the shrinkage Linear Discriminate Analysis (LDA) classifier. The second and third fixations in the triplets were classified at lower rate. We expect that, with improved feature sets and classifiers, a hybrid dwell-based Eye-Brain-Computer Interface (EBCI) can be built using the FRP difference between the intended and spontaneous fixations. If this direction of BCI development will be successful, such a multimodal interface may improve the fluency of interaction and can possibly become the basis for a new input device for paralyzed and healthy users, the EBCI "Wish Mouse."

Brain-computer interfaces in the completely locked-in state and chronic stroke

Brain-computer interfaces (BCIs) use brain activity to control external devices, facilitating paralyzed patients to interact with the environment. In this chapter, we discuss the historical perspective of development of BCIs and the current advances of noninvasive BCIs for communication in patients with amyotrophic lateral sclerosis and for restoration of motor impairment after severe stroke. Distinct techniques have been explored to control a BCI in patient population especially electroencephalography (EEG) and more recently near-infrared spectroscopy (NIRS) because of their noninvasive nature and low cost. Previous studies demonstrated successful communication of patients with locked-in state (LIS) using EEG- and invasive electrocorticography-BCI and intracortical recordings when patients still showed residual eye control, but not with patients with complete LIS (ie, complete paralysis). Recently, a NIRS-BCI and classical conditioning procedure was introduced, allowing communication in patients in the complete locked-in state (CLIS). In severe chronic stroke without residual hand function first results indicate a possible superior motor rehabilitation to available treatment using BCI training. Here we present an overview of the available studies and recent results, which open new doors for communication, in the completely paralyzed and rehabilitation in severely affected stroke patients. We also reflect on and describe possible neuronal and learning mechanisms responsible for BCI control and perspective for future BMI research for communication in CLIS and stroke motor recovery.

An Auditory Brain-Computer Interface Based on the Self-Regulation of Slow Cortical Potentials

Objectives. Communication support for severely paralyzed patients with visual impairment is needed. Therefore, the feasibility of a brain-computer interface (BCI) using auditory stimuli alone, based on the self-regulation of slow cortical potentials (SCPs), was investigated. Methods. Auditory stimuli were used for task and feedback presentation in an SCP self-regulation paradigm. Voluntarily produced SCP responses and measures of communication performance were compared between 3 groups (total of N = 59) of visual, auditory, and cross-modal visual-auditory modality. Electroencephalogram recordings and training from Cz-mastoids were carried out on 3 consecutive sessions. Data of 1500 trials per subject were collected. Results. Best performance was achieved for the visual, followed by the auditory condition. The performance deficit of the auditory condition was partly due to decreased self-produced positivity. Larger SCP response variability also accounted for lower performance of the auditory condition. Cross-modally presented stimuli did not lead to significant learning and control of SCP. Conclusions. Brain-computer communication using auditory stimuli only is possible. Smaller cortical positivity achieved in the auditory condition, as compared to the visual condition, may be a consequence of increased selective attention to simultaneously presented auditory stimuli. To optimize performance, auditory stimuli characteristics may have to be adapted. Other suggestions for enhancement of communication performance with auditory stimuli are discussed.

'Catching the waves' - slow cortical potentials as moderator of voluntary action

The readiness potential is an ongoing negativity in the EEG preceding a self-initiated movement by approximately 1.5s. So far it has predominantly been interpreted as a preparatory signal with a causal link to the upcoming movement. Here a different hypothesis is suggested which we call the selective slow cortical potential sampling hypothesis. In this review of recent research results we argue that the initiation of a voluntary action is more likely during negative fluctuations of the slow cortical potential and that the sampling and averaging of many trials leads to the observed negativity. That is, empirical evidence indicates that the early readiness potential is not a neural correlate of preconscious motor preparation and thus a determinant of action. Our hypothesis thereafter challenges the classic interpretation of the Libet experiment which is often taken as proof that there is no free will. We furthermore suggest that slow cortical potentials are related to an urge to act but are not a neural indicator of the decision process of action initiation.

Brain-computer interfaces for patients with disorders of consciousness

The disorders of consciousness refer to clinical conditions that follow a severe head injury. Patients diagnosed as in a vegetative state lack awareness, while patients diagnosed as in a minimally conscious state retain fluctuating awareness. However, it is a challenge to accurately diagnose these disorders with clinical assessments of behavior. To improve diagnostic accuracy, neuroimaging-based approaches have been developed to detect the presence or absence of awareness in patients who lack overt responsiveness. For the small subset of patients who retain awareness, brain-computer interfaces could serve as tools for communication and environmental control. Here we review the existing literature concerning the sensory and cognitive abilities of patients with disorders of consciousness with respect to existing brain-computer interface designs. We highlight the challenges of device development for this special population and address some of the most promising approaches for future investigations.

Electrophysiological CNS-processes related to associative learning in humans

The neurophysiology of human associative memory has been studied with electroencephalographic techniques since the 1930s. This research has revealed that different types of electrophysiological processes in the human brain can be modified by conditioning: sensory evoked potentials, sensory induced gamma-band activity, periods of frequency-specific waves (alpha and beta waves, the sensorimotor rhythm and the mu-rhythm) and slow cortical potentials. Conditioning of these processes has been studied in experiments that either use operant conditioning or repeated contingent pairings of conditioned and unconditioned stimuli (classical conditioning). In operant conditioning, the appearance of a specific brain process is paired with an external stimulus (neurofeedback) and the feedback enables subjects to obtain varying degrees of control of the CNS-process. Such acquired self-regulation of brain activity has found practical uses for instance in the amelioration of epileptic seizures, Autism Spectrum Disorders (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). It has also provided communicative means of assistance for tetraplegic patients through the use of brain computer interfaces. Both extra and intracortically recorded signals have been coupled with contingent external feedback. It is the aim for this review to summarize essential results on all types of electromagnetic brain processes that have been modified by classical or operant conditioning. The results are organized according to type of conditioned EEG-process, type of conditioning, and sensory modalities of the conditioning stimuli.

Brain computer interface learning for systems based on electrocorticography and intracortical microelectrode arrays

A brain-computer interface (BCI) system transforms neural activity into control signals for external devices in real time. A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively. We call this process BCI learning, and it often requires significant effort and time. Therefore, it is important to study this process and develop novel and efficient approaches to accelerate BCI learning. This article reviews major approaches that have been used for BCI learning, including computer-assisted learning, co-adaptive learning, operant conditioning, and sensory feedback. We focus on BCIs based on electrocorticography and intracortical microelectrode arrays for restoring motor function. This article also explores the possibility of brain modulation techniques in promoting BCI learning, such as electrical cortical stimulation, transcranial magnetic stimulation, and optogenetics. Furthermore, as proposed by recent BCI studies, we suggest that BCI learning is in many ways analogous to motor and cognitive skill learning, and therefore skill learning should be a useful metaphor to model BCI learning.

Preprocessing by a Bayesian single-trial event-related potential estimation technique allows feasibility of an assistive single-channel P300-based brain-computer interface

A major clinical goal of brain-computer interfaces (BCIs) is to allow severely paralyzed patients to communicate their needs and thoughts during their everyday lives. Among others, P300-based BCIs, which resort to EEG measurements, have been successfully operated by people with severe neuromuscular disabilities. Besides reducing the number of stimuli repetitions needed to detect the P300, a current challenge in P300-based BCI research is the simplification of system's setup and maintenance by lowering the number N of recording channels. By using offline data collected in 30 subjects (21 amyotrophic lateral sclerosis patients and 9 controls) through a clinical BCI with N = 5 channels, in the present paper we show that a preprocessing approach based on a Bayesian single-trial ERP estimation technique allows reducing N to 1 without affecting the system's accuracy. The potentially great benefit for the practical usability of BCI devices (including patient acceptance) that would be given by the reduction of the number N of channels encourages further development of the present study, for example, in an online setting.

Learned regulation of brain metabolism

Self-regulation and voluntary control of circumscribed brain regions using real-time functional MRI (rt-fMRI) allows the establishment of a causal functional link between localized brain activity and behavior and cognition. A long tradition of research has clearly shown the brain's ability to learn volitional control of its own activity and effects on behavior. Yet, the underlying neural mechanism of self-regulation is still not fully understood. Here, we propose that self-regulation of brain activity is akin to skill learning and thus may depend on an intact subcortical motor system. We elaborate on the critical role of the basal ganglia in skill learning and neurofeedback, and clarify that brain-self-regulation need not be an explicit and conscious process as often mistakenly held.

Toward functioning and usable brain-computer interfaces (BCIs): a literature review

PURPOSE

The aim of this paper is to provide an exhaustive review of the literature about brain-computer interfaces (BCIs) that could be used with these paralysed patients. The electroencephalography (EEG) is the best candidate for the continuous use in the environment of patients' houses, due to its portability and ease of use. For this reason, the present paper will focus on this kind of BCI. Moreover, it is our aim to focus more on the patients, regarding their active role in the modulation of the brain activity. This leads to a differentiation between studies that use an active regulation and studies that use a non-active regulation.

METHOD

Relevant articles in the BCIs field were selected using MEDLINE and PsycINFO.

RESULTS

Research through data banks produced 980 results, which were reduced to 127 after exclusion criteria selection. These references were divided in four categories, based on the use of active or non-active regulation, and on the event related potential used.

CONCLUSIONS

In most of the examined works, the focus was on the development of systems and algorithms able to recognise and classify brain events. Although this kind of research is fundamental, a user-centred point of view was rarely adopted. [Box: see text].

Dysarthria in amyotrophic lateral sclerosis: A review

Dysarthria is a motor disorder of speech characterized by abnormalities of the articulation and intelligibility of speech. Phonation and the rate of facial movements may also be affected. Understanding the nature and course of dysarthria in amyotrophic lateral sclerosis (ALS) is important because loss of communication prevents patients from participating in many activities, may lead to social isolation, and reduces the quality of life. The goal of management of dysarthria in ALS patients is to optimize communication effectiveness for as long as possible. The information about dysarthria in ALS is dispersed in physiological, pathological, speech therapy, otorhinolaringological and neurological publications. This review summarizes the current state of knowledge on the clinical features, differential diagnosis, pathophysiology, investigations and management of dysarthria in ALS patients. There is a need to compare the different methods used to assess dysarthria and for controlled clinical trials to assess therapeutic strategies.

Brain-computer interface systems: progress and prospects

Brain-computer interface (BCI) systems support communication through direct measures of neural activity without muscle activity. BCIs may provide the best and sometimes the only communication option for users disabled by the most severe neuromuscular disorders and may eventually become useful to less severely disabled and/or healthy individuals across a wide range of applications. This review discusses the structure and functions of BCI systems, clarifies terminology and addresses practical applications. Progress and opportunities in the field are also identified and explicated.

Neurofeedback for Children with ADHD: A Comparison of SCP and Theta/Beta Protocols

Behavioral and cognitive improvements in children with ADHD have been consistently reported after neurofeedback-treatment. However, neurofeedback has not been commonly accepted as a treatment for ADHD. This study addresses previous methodological shortcomings while comparing a neurofeedback-training of Theta-Beta frequencies and training of slow cortical potentials (SCPs). The study aimed at answering (a) whether patients were able to demonstrate learning of cortical self-regulation, (b) if treatment leads to an improvement in cognition and behavior and (c) if the two experimental groups differ in cognitive and behavioral outcome variables. SCP participants were trained to produce positive and negative SCP-shifts while the Theta/Beta participants were trained to suppress Theta (4-8 Hz) while increasing Beta (12-20 Hz). Participants were blind to group assignment. Assessment included potentially confounding variables. Each group was comprised of 19 children with ADHD (aged 8-13 years). The treatment procedure consisted of three phases of 10 sessions each. Both groups were able to intentionally regulate cortical activity and improved in attention and IQ. Parents and teachers reported significant behavioral and cognitive improvements. Clinical effects for both groups remained stable six months after treatment. Groups did not differ in behavioural or cognitive outcome.

Breaking the silence: brain-computer interfaces (BCI) for communication and motor control

Brain-computer interfaces (BCI) allow control of computers or external devices with regulation of brain activity alone. Invasive BCIs, almost exclusively investigated in animal models using implanted electrodes in brain tissue, and noninvasive BCIs using electrophysiological recordings in humans are described. Clinical applications were reserved with few exceptions for the noninvasive approach: communication with the completely paralyzed and locked-in syndrome with slow cortical potentials, sensorimotor rhythm and P300, and restoration of movement and cortical reorganization in high spinal cord lesions and chronic stroke. It was demonstrated that noninvasive EEG-based BCIs allow brain-derived communication in paralyzed and locked-in patients but not in completely locked-in patients. At present no firm conclusion about the clinical utility of BCI for the control of voluntary movement can be made. Invasive multielectrode BCIs in otherwise healthy animals allowed execution of reaching, grasping, and force variations based on spike patterns and extracellular field potentials. The newly developed fMRI-BCIs and NIRS-BCIs, like EEG BCIs, offer promise for the learned regulation of emotional disorders and also disorders of young children.

Brain plasticity: From pathophysiological mechanisms to therapeutic applications

Cerebral plasticity, which is the dynamic potential of the brain to reorganize itself during ontogeny, learning, or following damage, has been widely studied in the last decade, in vitro, in animals, and also in humans since the development of functional neuroimaging. In the first part of this review, the main hypotheses about the pathophysiological mechanisms underlying plasticity are presented. At a microscopic level, modulations of synaptic efficacy, unmasking of latent connections, phenotypic modifications and neurogenesis have been identified. At a macroscopic level, diaschisis, functional redundancies, sensory substitution and morphological changes have been described. In the second part, the behavioral consequences of such cerebral phenomena in physiology, namely the "natural" plasticity, are analyzed in humans. The review concludes on the therapeutic implications provided by a better understanding of these mechanisms of brain reshaping. Indeed, this plastic potential might be 'guided' in neurological diseases, using rehabilitation, pharmacological drugs, transcranial magnetic stimulation, neurosurgical methods, and even new techniques of brain-computer interface - in order to improve the quality of life of patients with damaged nervous systems.

Analyzing Trends in Brain Interface Technology: A Method to Compare Studies

Continued progress in the field of Brain Interface (BI) research has encouraged the rapid expansion of the BI community over the last two decades. As the number of BI researchers and organizations steadily increases, newer and more advanced technologies are constantly produced, evaluated, and reported. Though the BI community is committed to accurate and objective evaluation of methods, systems, and technology, the diversity of the field has hindered the development of objective methods of comparison. This paper introduces a new method for directly comparing studies of BI technology based on the theoretical models and taxonomy proposed by Mason, Moore, and Birch. The effectiveness of the proposed method was demonstrated by interpreting and comparing a representative set of 21 BI studies. The method allowed us to 1) identify the salient aspects of a specific BI study, 2) identify what has been reported and what has been omitted, 3) facilitate a complete and objective comparison with other studies, and 4) characterize overall trends, areas of inactivity, and reporting practices.

A general framework for characterizing studies of brain interface technology

The development of brain interface (BI) technology continues to attract researchers with a wide range of backgrounds and expertise. Though the BI community is committed to accurate and objective evaluation of methods, systems, and technology, the very diversity of the methods and terminology used in the field hinders understanding and impairs technology cross-fertilization and cross-group validation of findings. Underlying this dilemma is a lack of common perspective and language. As seen in our previous works in this area, our approach to remedy this problem is to propose language in the form of taxonomy and functional models. Our intent is to document and validate our best thinking in this area and publish a perspective that will stimulate discussion. We encourage others to do the same with the belief that focused discussion on language issues will accelerate the inherently slow natural evolution of language selection and thus alleviate related problems. In this work, we propose a theoretical framework for describing BI-technology-related studies. The proposed framework is based on the theoretical concepts and terminology from classical science, assistive technology development, human-computer interaction, and previous BI-related works. Using a representative set of studies from the literature, the proposed BI study framework was shown to be complete and appropriate perspective for thoroughly characterizing a BI study. We have also demonstrated that this BI study framework is useful for (1) objectively reviewing existing BI study designs and results, (2) comparing designs and results of multiple BI studies, (3) designing new studies or objectively reporting BI study results, and (4) facilitating intra- and inter-group communication and the education of new researchers. As such, it forms a sound and appropriate basis for community discussion.

Physiological regulation of thinking: brain–computer interface (BCI) research

The discovery of event-related desynchronization (ERD) and event-related synchronization (ERS) by Pfurtscheller paved the way for the development of brain-computer interfaces (BCIs). BCIs allow control of computers or external devices with the regulation of brain activity only. Two different research traditions produced two different types of BCIs: invasive BCIs, realized with implanted electrodes in brain tissue and noninvasive BCIs using electrophysiological recordings in humans such as electroencephalography (EEG) and magnetoencephalography (MEG) and metabolic changes such as functional magnetic resonance imaging (fMRI) and near infrared spectroscopy (NIRS). Clinical applications were reserved with few exceptions for the noninvasive approach: communication with the completely paralyzed and locked-in syndrome with slow cortical potentials (SCPs), sensorimotor rhythms (SMRs), and P300 and restoration of movement and cortical reorganization in high spinal cord lesions and chronic stroke. It was demonstrated that noninvasive EEG-based BCIs allow brain-derived communication in paralyzed and locked-in patients. Movement restoration was achieved with noninvasive BCIs based on SMRs control in single cases with spinal cord lesions and chronic stroke. At present no firm conclusion about the clinical utility of BCI for the control of voluntary movement can be made. Invasive multielectrode BCIs in otherwise healthy animals allowed execution of reaching, grasping, and force variations from spike patterns and extracellular field potentials. Whether invasive approaches allow superior brain control of motor responses compared to noninvasive BCI with intelligent peripheral devices and electrical muscle stimulation and EMG feedback remains to be demonstrated. The newly developed fMRI-BCIs and NIRS-BCIs offer promise for the learned regulation of emotional disorders and also disorders of small children (in the case of NIRS).

Predictability of Brain-Computer Communication

Abstract Since 1996 we have been teaching more than 18 severely or totally paralyzed patients to successfully control the movements of a cursor on a computer screen by means of systematic changes in the amplitudes of their slow cortical potentials (SCPs; Birbaumer, Ghanayim, Hinterberger, Iversen, Kotchoubey et al., 1999 ). Patients learned regulation of their SCP amplitudes by means of a brain-computer interface (BCI) and on-line feedback about the time course of SCP amplitude shifts, represented by cursor movements on a computer screen. When patients were able to successfully regulate their SCP amplitude, they were trained to use this ability to communicate with friends and caregivers by means of a Language Support Program ( Perelmouter, Kotchoubey, Kübler, Taub, & Birbaumer, 1999 ). Having a reliable predictor of progress in training would be particularly helpful because training patients at their homes requires substantial effort and a positive outcome is desirable given limited personal and financial resources. In this study we present data from healthy participants (n = 10) and a sample of patients (n = 10), diagnosed with amyotrophic lateral sclerosis, who participated in six BCI training sessions; six patients continued training for another six sessions. All participants except one achieved stable significant cursor control. The number of sessions needed to achieve significant cursor control (initial training phase) correlated moderately with the number of sessions needed to achieve a correct response rate of 70% (advanced training phase). Individual differences in performance remained stable within the six training sessions. After six sessions both groups had achieved significant cursor control, but patients' performance was poorer than that of healthy participants. The patients, however, were trained once a week only, and for some patients longer breaks in training occurred. We conclude that learning during the initial training phase indicates the duration of training that will be necessary to achieve 70% correct responses. A higher frequency of training sessions per week seems necessary to achieve faster progress.