Improving speed and accuracy of brain-computer interfaces using readiness potential features

Cortico-muscular coupling to control a hybrid brain-computer interface for upper limb motor rehabilitation: A pseudo-online study on stroke patients

Brain-Computer Interface (BCI) systems for motor rehabilitation after stroke have proven their efficacy to enhance upper limb motor recovery by reinforcing motor related brain activity. Hybrid BCIs (h-BCIs) exploit both central and peripheral activation and are frequently used in assistive BCIs to improve classification performances. However, in a rehabilitative context, brain and muscular features should be extracted to promote a favorable motor outcome, reinforcing not only the volitional control in the central motor system, but also the effective projection of motor commands to target muscles, i.e., central-to-peripheral communication. For this reason, we considered cortico-muscular coupling (CMC) as a feature for a h-BCI devoted to post-stroke upper limb motor rehabilitation. In this study, we performed a pseudo-online analysis on 13 healthy participants (CTRL) and 12 stroke patients (EXP) during executed (CTRL, EXP unaffected arm) and attempted (EXP affected arm) hand grasping and extension to optimize the translation of CMC computation and CMC-based movement detection from offline to online. Results showed that updating the CMC computation every 125 ms (shift of the sliding window) and accumulating two predictions before a final classification decision were the best trade-off between accuracy and speed in movement classification, independently from the movement type. The pseudo-online analysis on stroke participants revealed that both attempted and executed grasping/extension can be classified through a CMC-based movement detection with high performances in terms of classification speed (mean delay between movement detection and EMG onset around 580 ms) and accuracy (hit rate around 85%). The results obtained by means of this analysis will ground the design of a novel non-invasive h-BCI in which the control feature is derived from a combined EEG and EMG connectivity pattern estimated during upper limb movement attempts.

Improving Covariance Matrices Derived from Tiny Training Datasets for the Classification of Event-Related Potentials with Linear Discriminant Analysis

Electroencephalogram data used in the domain of brain–computer interfaces typically has subpar signal-to-noise ratio and data acquisition is expensive. An effective and commonly used classifier to discriminate event-related potentials is the linear discriminant analysis which, however, requires an estimate of the feature distribution. While this information is provided by the feature covariance matrix its large number of free parameters calls for regularization approaches like Ledoit–Wolf shrinkage. Assuming that the noise of event-related potential recordings is not time-locked, we propose to decouple the time component from the covariance matrix of event-related potential data in order to further improve the estimates of the covariance matrix for linear discriminant analysis. We compare three regularized variants thereof and a feature representation based on Riemannian geometry against our proposed novel linear discriminant analysis with time-decoupled covariance estimates. Extensive evaluations on 14 electroencephalogram datasets reveal, that the novel approach increases the classification performance by up to four percentage points for small training datasets, and gracefully converges to the performance of standard shrinkage-regularized LDA for large training datasets. Given these results, practitioners in this field should consider using our proposed time-decoupled covariance estimation when they apply linear discriminant analysis to classify event-related potentials, especially when few training data points are available.

High Amplitude EEG Motor Potential during Repetitive Foot Movement: Possible Use and Challenges for Futuristic BCIs That Restore Mobility after Spinal Cord Injury

Recent advances in neuroprostheses provide us with promising ideas of how to improve the quality of life in people suffering from impaired motor functioning of upper and lower limbs. Especially for patients after spinal cord injury (SCI), futuristic devices that are controlled by thought via brain-computer interfaces (BCIs) might be of tremendous help in managing daily tasks and restoring at least some mobility. However, there are certain problems arising when trying to implement BCI technology especially in such a heterogenous patient group. A plethora of processes occurring after the injuries change the brain's structure as well as its functionality collectively referred to as neuroplasticity. These changes are very different between individuals, leading to an increasing interest to reveal the exact changes occurring after SCI. In this study we investigated event-related potentials (ERPs) derived from electroencephalography (EEG) signals recorded during the (attempted) execution and imagination of hand and foot movements in healthy subjects and patients with SCI. As ERPs and especially early components are of interest for BCI research we aimed to investigate differences between 22 healthy volunteers and 7 patients (mean age = 51.86, SD = 15.49) suffering from traumatic or non-traumatic SCI since 2–314 months (mean = 116,57, SD = 125,55). We aimed to explore differences in ERP responses as well as the general presence of component that might be of interest to further consider for incorporation into BCI research. In order to match the real-life situation of BCIs for controlling neuroprostheses, we worked on small trial numbers (<25), only. We obtained a focal potential over Pz in ten healthy participants but in none of the patients after lenient artifact rejection. The potential was characterized by a high amplitude, it correlated with the repeated movements (6 times in 6 s) and in nine subjects it significantly differed from a resting condition. Furthermore, there are strong arguments against possible confounding factors leading to the potential's appearance. This phenomenon, occurring when movements are repeatedly conducted, might represent a possible potential to be used in futuristic BCIs and further studies should try to investigate the replicability of its appearance.

The point of no return in vetoing self-initiated movements

In humans, spontaneous movements are often preceded by early brain signals. One such signal is the readiness potential (RP) that gradually arises within the last second preceding a movement. An important question is whether people are able to cancel movements after the elicitation of such RPs, and if so until which point in time. Here, subjects played a game where they tried to press a button to earn points in a challenge with a brain-computer interface (BCI) that had been trained to detect their RPs in real time and to emit stop signals. Our data suggest that subjects can still veto a movement even after the onset of the RP. Cancellation of movements was possible if stop signals occurred earlier than 200 ms before movement onset, thus constituting a point of no return.

Categorical vowel perception enhances the effectiveness and generalization of auditory feedback in human-machine-interfaces

Human-machine interface (HMI) designs offer the possibility of improving quality of life for patient populations as well as augmenting normal user function. Despite pragmatic benefits, utilizing auditory feedback for HMI control remains underutilized, in part due to observed limitations in effectiveness. The goal of this study was to determine the extent to which categorical speech perception could be used to improve an auditory HMI. Using surface electromyography, 24 healthy speakers of American English participated in 4 sessions to learn to control an HMI using auditory feedback (provided via vowel synthesis). Participants trained on 3 targets in sessions 1–3 and were tested on 3 novel targets in session 4. An “established categories with text cues” group of eight participants were trained and tested on auditory targets corresponding to standard American English vowels using auditory and text target cues. An “established categories without text cues” group of eight participants were trained and tested on the same targets using only auditory cuing of target vowel identity. A “new categories” group of eight participants were trained and tested on targets that corresponded to vowel-like sounds not part of American English. Analyses of user performance revealed significant effects of session and group (established categories groups and the new categories group), and a trend for an interaction between session and group. Results suggest that auditory feedback can be effectively used for HMI operation when paired with established categorical (native vowel) targets with an unambiguous cue.

Classification of resting, anticipation and movement states in self-initiated arm movements for EEG brain computer interfaces

In the last years, there has been an increasing interest in using Brain Computer Interfaces (BCI) within motor rehabilitation therapies that use robotic devices or functional electro stimulation to help or guide the efforts of the patient to move her body. A crucial step of these therapies is to provide help to the user just when she is actually trying to accomplish a certain motion or task One of the most promising applications of BCI systems in this context is its ability to measure the user intentions and actions to trigger the rehabilitation devices accordingly. This paper studies the single-trial classification based on EEG measurements of three basic states during the execution of self-initiated motion: rest, motion preparation (or anticipation) and motion. We conducted an experiment where the participants had to reach at their will eight different locations from a fixed starting position. Results for seven healthy subjects show that it is possible to achieve good classification rates given that features are carefully selected for each subject and for each pair of states.

Identification of task parameters from movement-related cortical potentials

The study investigates the accuracy in discriminating rate of torque development (RTD) and target torque (TT) (task parameters) from electroencephalography (EEG) signals generated during imaginary motor tasks. Signals were acquired from nine healthy subjects during four imaginary isometric plantar-flexions of the right foot involving two RTDs (ballistic and moderate) and two TTs (30 and 60% of the maximal voluntary contraction torque), each repeated 60 times in random order. The single-trial EEG traces were classified with a pattern recognition approach based on wavelet coefficients as features and support vector machine (SVM) as classifier. Average misclassification rates were (mean +/- SD) 16 +/- 9% and 26 +/- 13% for discrimination of the two TTs under ballistic and moderate RTDs, respectively. RTDs could be discriminated with misclassification rates of 16 +/- 11% and 19 +/- 10% under high and low TT, respectively. These results indicate that differences in both TT and RTD can be detected from single-trial EEG traces during imaginary tasks.

Evaluating User Experience of Actual and Imagined Movement in BCI Gaming

Most research on Brain-Computer Interfaces (BCI) focuses on developing ways of expression for disabled people who are not able to communicate through other means. Recently it has been shown that BCI can also be used in games to give users a richer experience and new ways to interact with a computer or game console. This paper describes research conducted to find out what the differences are between using actual and imagined movement as modalities in a BCI game. Results show that there are significant differences in user experience and that actual movement is a more robust way of communicating through a BCI.

A self-paced brain interface system that uses movement related potentials and changes in the power of brain rhythms

Movement execution results in the simultaneous generation of movement-related potentials (MRP) as well as changes in the power of Mu and Beta rhythms. This paper proposes a new self-paced multi-channel BI that combines features extracted from MRPs and from changes in the power of Mu and Beta rhythms. We developed a new algorithm to classify the high-dimensional feature space. It uses a two-stage multiple-classifier system (MCS). First, an MCS classifies each neurological phenomenon separately using the information extracted from specific EEG channels (EEG channels are selected by a genetic algorithm). In the second stage, another MCS combines the outputs of MCSs developed in the first stage. Analysis of the data of four able-bodied subjects shows the superior performance of the proposed algorithm compared with a scheme where the features were all combined in a single feature vector and then classified.

A Comprehensive Survey of Brain Interface Technology Designs

In this work we present the first comprehensive survey of Brain Interface (BI) technology designs published prior to January 2006. Detailed results from this survey, which was based on the Brain Interface Design Framework proposed by Mason and Birch, are presented and discussed to address the following research questions: (1) which BI technologies are directly comparable, (2) what technology designs exist, (3) which application areas (users, activities and environments) have been targeted in these designs, (4) which design approaches have received little or no research and are possible opportunities for new technology, and (5) how well are designs reported. The results of this work demonstrate that meta-analysis of high-level BI design attributes is possible and informative. The survey also produced a valuable, historical cross-reference where BI technology designers can identify what types of technology have been proposed and by whom.

Automatic user customization for improving the performance of a self-paced brain interface system

Customizing the parameter values of brain interface (BI) systems by a human expert has the advantage of being fast and computationally efficient. However, as the number of users and EEG channels grows, this process becomes increasingly time consuming and exhausting. Manual customization also introduces inaccuracies in the estimation of the parameter values. In this paper, the performance of a self-paced BI system whose design parameter values were automatically user customized using a genetic algorithm (GA) is studied. The GA automatically estimates the shapes of movement-related potentials (MRPs), whose features are then extracted to drive the BI. Offline analysis of the data of eight subjects revealed that automatic user customization improved the true positive (TP) rate of the system by an average of 6.68% over that whose customization was carried out by a human expert, i.e., by visually inspecting the MRP templates. On average, the best improvement in the TP rate (an average of 9.82%) was achieved for four individuals with spinal cord injury. In this case, the visual estimation of the parameter values of the MRP templates was very difficult because of the highly noisy nature of the EEG signals. For four able-bodied subjects, for which the MRP templates were less noisy, the automatic user customization led to an average improvement of 3.58% in the TP rate. The results also show that the inter-subject variability of the TP rate is also reduced compared to the case when user customization is carried out by a human expert. These findings provide some primary evidence that automatic user customization leads to beneficial results in the design of a self-paced BI for individuals with spinal cord injury.

On-line differentiation of neuroelectric activities: algorithms and applications

This paper discusses machine learning methods and their application to Brain-Computer Interfacing. A particular focus is placed on linear classification methods which can be applied in the BCI context. Finally, we provide an overview of the Berlin-Brain Computer Interface (BBCI).