In recent years, new research has brought the field of electroencephalogram (EEG)-based brain–computer interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely, “Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user–machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human–computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices.

Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges.

Advances in brain science and computer technology in the past decade have led to exciting developments in brain-computer interface (BCI), thereby making BCI a top research area in applied science. The renaissance of BCI opens new methods of neurorehabilitation for physically disabled people (e.g. paralyzed patients and amputees) and patients with brain injuries (e.g. stroke patients). Recent technological advances such as wireless recording, machine learning analysis, and real-time temporal resolution have increased interest in electroencephalographic (EEG) based BCI approaches. Many BCI studies have focused on decoding EEG signals associated with whole-body kinematics/kinetics, motor imagery, and various senses. Thus, there is a need to understand the various experimental paradigms used in EEG-based BCI systems. Moreover, given that there are many available options, it is essential to choose the most appropriate BCI application to properly manipulate a neuroprosthetic or neurorehabilitation device. The current review evaluates EEG-based BCI paradigms regarding their advantages and disadvantages from a variety of perspectives. For each paradigm, various EEG decoding algorithms and classification methods are evaluated. The applications of these paradigms with targeted patients are summarized. Finally, potential problems with EEG-based BCI systems are discussed, and possible solutions are proposed.

A comprehensive review of EEG-based brain-computer interface paradigms.

It is generally thought that the signal-to-noise ratio, the bandwidth, and the information content of neural data acquired via noninvasive scalp electroencephalography (EEG) are insufficient to extract detailed information about natural, multijoint movements of the upper limb. Here, we challenge this assumption by continuously decoding three-dimensional (3D) hand velocity from neural data acquired from the scalp with 55-channel EEG during a 3D center-out reaching task. To preserve ecological validity, five subjects self-initiated reaches and self-selected targets. Eye movements were controlled so they would not confound the interpretation of the results. With only 34 sensors, the correlation between measured and reconstructed velocity profiles compared reasonably well to that reported by studies that decoded hand kinematics from neural activity acquired intracranially. We subsequently examined the individual contributions of EEG sensors to decoding to find substantial involvement of scalp areas over the sensorimotor cortex contralateral to the reaching hand. Using standardized low-resolution brain electromagnetic tomography (sLORETA), we identified distributed current density sources related to hand velocity in the contralateral precentral gyrus, postcentral gyrus, and inferior parietal lobule. Furthermore, we discovered that movement variability negatively correlated with decoding accuracy, a finding to consider during the development of brain-computer interface systems. Overall, the ability to continuously decode 3D hand velocity from EEG during natural, center-out reaching holds promise for the furtherance of noninvasive neuromotor prostheses for movement-impaired individuals.

https://www.jneurosci.org/content/jneuro/30/9/3432.full.pdf

Reconstructing Three-Dimensional Hand Movements from Noninvasive Electroencephalographic Signals

Goal: Sensorimotor-based brain–computer interfaces (BCIs) have achieved successful control of real and virtual devices in up to three dimensions; however, the traditional sensor-based paradigm limits the intuitive use of these systems. Many control signals for state-of-the-art BCIs involve imagining the movement of body parts that have little to do with the output command, revealing a cognitive disconnection between the user's intent and the action of the end effector. Therefore, there is a need to develop techniques that can identify with high spatial resolution the self-modulated neural activity reflective of the actions of a helpful output device. Methods: We extend previous EEG source imaging (ESI) work to decoding natural hand/wrist manipulations by applying a novel technique to classifying four complex motor imaginations of the right hand: flexion, extension, supination, and pronation. Results: We report an increase of up to 18.6% for individual task classification and 12.7% for overall classification using the proposed ESI approach over the traditional sensor-based method. Conclusion: ESI is able to enhance BCI performance of decoding complex right-hand motor imagery tasks. Significance: This study may lead to the development of BCI systems with naturalistic and intuitive motor imaginations, thus facilitating broad use of noninvasive BCIs.

EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks

In monkeys, the repeated activation of somatosensory afferents projecting onto the motor cortex (M1) has a pivotal role in motor skill learning. Here we investigate if sensory feedback that is artificially generated at specific times during imagination of a dorsiflexion task leads to reorganization of the human M1. The common peroneal nerve was stimulated to generate an afferent volley timed to arrive during specific phases of the cortical potential generated when a movement was imagined (50 repetitions). The change in the output of M1 was quantified by applying single transcranial magnetic stimuli to the area of M1 controlling the tibialis anterior muscle. The results demonstrated that the concomitance between the cognitive process of movement (motor imagination) and the ascending volley due to the peripheral nerve stimulation can lead to significant increases in cortical excitability. These increases were critically dependent on the timing between the peripherally generated afferent volley and the cortical potential generated during the imagined movement. Only if the afferent volley arrived during the peak negative deflection of the potential, were significant alterations in motor cortical output attained. These results demonstrate that an artificially generated signal (the peripheral afferent volley) can interact with a physiologically generated signal in humans leading to plastic changes within the M1, the final output stage for movement generation within the human brain. The results presented may have implications in systems for artificially inducing cortical plasticity in patients with motor impairments (neuromodulation).

https://physoc.onlinelibrary.wiley.com/doi/pdf/10.1113/jphysiol.2011.222851

Precise temporal association between cortical potentials evoked by motor imagination and afference induces cortical plasticity.

Single-trial discrimination of type and speed of wrist movements from EEG recordings.

The study investigated the possibility of identifying the speed of an imagined movement from EEG recordings in amyotrophic lateral sclerosis (ALS) patients. EEG signals were acquired from four ALS patients during imagination of wrist extensions at two speeds (fast and slow), each repeated up to 100 times in random order. The movement-related cortical potentials (MRCPs) and averaged sensorimotor rhythm associated with the two tasks were obtained from the EEG recordings. Moreover, offline single-trial EEG classification was performed with discrete wavelet transform for feature extraction and support vector machine for classification. The speed of the task was encoded in the time delay of peak negativity in the MRCPs, which was shorter for faster than for slower movements. The average single-trial misclassification rate between speeds was 30.4 ± 3.5 % when the best scalp location and time interval were selected for each individual. The scalp location and time interval leading to the lowest misclassification rate varied among patients. The results indicate that the imagination of movements at different speeds is a viable strategy for controlling a brain-computer interface system by ALS patients.

/pdf/offline-identification-of-imagined-speed-of-wrist-movements-4evw1gw94v.pdf

Offline identification of imagined speed of wrist movements in paralyzed ALS patients from single-trial EEG

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.

Identification of task parameters from movement-related cortical potentials

Objective: To understand the brain motor functions and neurophysiological changes due to motor disorder by comparing electroencephalographic data between healthy people and amyotrophic lateral sclerosis (ALS) patients. Methods: The movement related cortical potential (MRCP) was recorded from seven healthy subjects and four amyotrophic lateral sclerosis (ALS) patients. They were asked to imagine right wrist extension at two speeds (fast and slow). The peak negativity (PN) and rebound rate (RR) were extracted from MRCP for comparison. Results: The statistical analysis has showed that there was no significant difference in PN between the healthy and the ALS subjects. However, the healthy subjects presented faster RR than ALS during both fast and slow movement imagination. Conclusions: The weaker rebound rate of ALS patients might reflect the impairment of motor output pathways or the degree of motor degeneration. Significance: The comparison between healthy people and ALS patients provides a way to explain the movement disorder through brain electrical signal. In addition, the charac-teristics of MRCP could be used to monitor and guide brain plasticity in patients.

/pdf/comparison-of-movement-related-cortical-potential-in-healthy-3kt6nv9saf.pdf

Comparison of movement related cortical potential in healthy people and amyotrophic lateral sclerosis patients.

Brain??-??computer interface (BCI) systems aim at providing a nonmuscular communication and control channel to patients with severe disabilities or at promoting neuroplasticity. Motor imagery is the most common approach to producing electroencephalogram (EEG) changes in EEG-based BCI research. This chapter discusses an alternative approach for distinguishing movement-related parameters, such as speed, for the same imagined task. This approach provides a more intuitive way of control for the user and increases the potential number of commands. The chapter explains a series of studies that analyze the characteristics of movement-related cortical potentials (MRCPs), and explores their potential application to BCI. It describes the characteristics of MRCPs and how these characteristics are modulated by movement parameters, such as speed and torque. It also discusses these results from the viewpoint of applying MRCPs generated during imagined movements of the same joint as control signals for BCIs.

/pdf/movement-related-cortical-potentials-and-their-application-1se1d3tt85.pdf

Ying Gu

Papers

Single-trial discrimination of type and speed of wrist movements from EEG recordings.

Offline identification of imagined speed of wrist movements in paralyzed ALS patients from single-trial EEG

Identification of task parameters from movement-related cortical potentials

Comparison of movement related cortical potential in healthy people and amyotrophic lateral sclerosis patients.

Movement-related cortical potentials and their application in brain–computer interfacing