EEG-Based Asynchronous BCI Controls Functional Electrical Stimulation in a Tetraplegic Patient

The present study reports on the use of an EEG-based asynchronous (uncued, user-driven) brain-computer interface (BCI) for the control of functional electrical stimulation (FES). By the application of FES, noninvasive restoration of hand grasp function in a tetraplegic patient was achieved. The patient was able to induce bursts of beta oscillations by imagination of foot movement. These beta oscillations were recorded in a one EEG-channel configuration, bandpass filtered and squared. When this beta activity exceeded a predefined threshold, a trigger for the FES was generated. Whenever the trigger was detected, a subsequent switching of a grasp sequence composed of 4 phases occurred. The patient was able to grasp a glass with the paralyzed hand completely on his own without additional help or other technical aids

Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of 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

Brain-Switches for Asynchronous Brain−Computer Interfaces: A Systematic Review

A brain–computer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever the user wants. However, the relatively low performance of an asynchronous BCI system is problematic because redundant BCI commands are required to correct false-positive operations. To significantly reduce the number of false-positive operations of an asynchronous BCI system, a two-step approach has been proposed using a brain-switch that first determines whether the user wants to use an asynchronous BCI system before the operation of the asynchronous BCI system. This study presents a systematic review of the state-of-the-art brain-switch techniques and future research directions. To this end, we reviewed brain-switch research articles published from 2000 to 2019 in terms of their (a) neuroimaging modality, (b) paradigm, (c) operation algorithm, and (d) performance

Affective Brain-Computer Interfaces Neuroscientific Approaches to Affect Detection

The brain is involved in the registration, evaluation, and representation of emotional events, and in the subsequent planning and execution of adequate actions. Novel interface technologies – so-called affective brain-computer interfaces (aBCI) - can use this rich neural information, occurring in response to affective stimulation, for the detection of the affective state of the user. This chapter gives an overview of the promises and challenges that arise from the possibility of neurophysiology-based affect detection, with a special focus on electrophysiological signals. After outlining the potential of aBCI relative to other sensing modalities, the reader is introduced to the neurophysiological and neurotechnological background of this interface technology. Potential application scenarios are situated in a general framework of brain-computer interfaces. Finally, the main scientific and technological challenges that have to be solved on the way toward reliable affective brain-computer interfaces are discussed

Brain-Computer Interface Controlled Functional Electrical Stimulation System for Ankle Movement

Abstract Background Many neurological conditions, such as stroke, spinal cord injury, and traumatic brain injury, can cause chronic gait function impairment due to foot-drop. Current physiotherapy techniques provide only a limited degree of motor function recovery in these individuals, and therefore novel therapies are needed. Brain-computer interface (BCI) is a relatively novel technology with a potential to restore, substitute, or augment lost motor behaviors in patients with neurological injuries. Here, we describe the first successful integration of a noninvasive electroencephalogram (EEG)-based BCI with a noninvasive functional electrical stimulation (FES) system that enables the direct brain control of foot dorsiflexion in able-bodied individuals. Methods A noninvasive EEG-based BCI system was integrated with a noninvasive FES system for foot dorsiflexion. Subjects underwent computer-cued epochs of repetitive foot dorsiflexion and idling while their EEG signals were recorded and stored for offline analysis. The analysis generated a prediction model that allowed EEG data to be analyzed and classified in real time during online BCI operation. The real-time online performance of the integrated BCI-FES system was tested in a group of five able-bodied subjects who used repetitive foot dorsiflexion to elicit BCI-FES mediated dorsiflexion of the contralateral foot. Results Five able-bodied subjects performed 10 alternations of idling and repetitive foot dorsifiexion to trigger BCI-FES mediated dorsifiexion of the contralateral foot. The epochs of BCI-FES mediated foot dorsifiexion were highly correlated with the epochs of voluntary foot dorsifiexion (correlation coefficient ranged between 0.59 and 0.77) with latencies ranging from 1.4 sec to 3.1 sec. In addition, all subjects achieved a 100% BCI-FES response (no omissions), and one subject had a single false alarm. Conclusions This study suggests that the integration of a noninvasive BCI with a lower-extremity FES system is feasible. With additional modifications, the proposed BCI-FES system may offer a novel and effective therapy in the neuro-rehabilitation of individuals with lower extremity paralysis due to neurological injuries

Controlling Assistive Machines in Paralysis Using Brain Waves and Other Biosignals

The extent to which humans can interact with machines significantly enhanced through inclusion of speech, gestures, and eye movements. However, these communication channels depend on a functional motor system. As many people suffer from severe damage of the motor system resulting in paralysis and inability to communicate, the development of brain-machine interfaces (BMI) that translate electric or metabolic brain activity into control signals of external devices promises to overcome this dependence. People with complete paralysis can learn to use their brain waves to control prosthetic devices or exoskeletons. However, information transfer rates of currently available noninvasive BMI systems are still very limited and do not allow versatile control and interaction with assistive machines. Thus, using brain waves in combination with other biosignals might significantly enhance the ability of people with a compromised motor system to interact with assistive machines. Here, we give an overview of the current state of assistive, noninvasive BMI research and propose to integrate brain waves and other biosignals for improved control and applicability of assistive machines in paralysis. Beside introducing an example of such a system, potential future developments are being discussed

EEG-based brain-computer interface with visual and haptic feedback

Tehokas koehenkilöiden oppiminen palautteen avulla on tärkeää aivokäyttöliittymä tutkimuksessa. Suurimmassa osassa aiemmista tutkimuksista koehenkilöt ovat saaneet palautteen visuaalisena; toiset palautemodaliteetit voisivat paremmin palvella potilaita, joilla on näkövammoja ja käyttäjiä, jotka tarvitsevat näkökykyä muualla. Aiemmissa tutkimuksissa auditiivinen palaute oli merkittävästi huonompi koehenkilöiden opetuksessa kuin visuaalinen palaute. Haptinen (tunto) palaute voisi sopia paremmin aivokäyttöliittymille. Kuusi liikuntakykyistä, ensikertalaista koehenkilöä saivat haptista tai visuaalista palautetta tai molempia erillisissä sessioissa opetellessaan kaksiluokkaisen aivokäyttöliittymän hallintaa vasemman ja oikean käden kuvittelulla. Kokeita varten toteutettu TKK BCI komponentteineen kykenee reaaliaikaiseen signaalin mittaukseen, signaalien käsittelyyn, palautteen antamiseen ja sovellusten ohjaamiseen. Palautetta annettiin kerran sekunnissa joko näytöllä tai haptisilla elementeillä, jotka kiinnitettiin koehenkilön kaulan alaosaan. Koehenkilöt saavuttivat keskimäärin 67 % luokittelutuloksia haptisella palautteella ja 68 % visuaalisella palautteella. Yksi koehenkilö saavutti jopa 88.8 % luokittelutuloksen yhdessä sessiossa. Piirrevalinnalla löydetyt vakaat sensorimotoriset rytmit taajuuksien 8-12 Hz ja 18-26 Hz välissä tuottivat parhaimmat tulokset. Haptinen stimulaatio aiheutti vain vähän näkyvää häiriötä taajuusalueella 8-30 Hz. Tulokset tästä tutkimuksessa näyttävät, ettei haptisen ja visuaalisen palautteen välillä ole selkeää eroa koehenkilöiden oppimisessa. Suurin osa koehenkilöistä kokivat haptisen palautteen luonnolliseksi ja miellyttäväksi. Haptinen palaute voi näistä seikoista johtuen korvata visuaalisen palautteen ja vapauttaa näkökyvyn muihin tehtäviin. Tulosten vahvistamiseksi on tarpeellista tehdä jatkotutkimuksia liikuntakyvyttömillä oikeissa kotiympäristöissä.Efficient training of subjects with feedback is essential to brain-computer interface (BCI) research. In most previous studies, subjects have mostly received visual feedback; other feedback modalities could, however, better serve patients with visual impairment and in tasks, which allocate visual attention. In previous studies auditory feedback was significantly worse than visual feedback during subject training. Haptic feedback (vibrotactile stimulation) could be better suited for brain-computer communication than auditory feedback. Six able-bodied subjects without previous BCI experience received haptic or visual feedback or both in separate sessions while learning to control a two-class BCI using imagery of left and right hand movements. A BCI system was designed and implemented for the experiments. The TKK BCI consists of components capable of real-time signal acquisition, signal processing, feedback, and control of applications. The feedback was presented once every second either on a screen or with haptic elements attached to the base of the subject's neck. The subjects achieved average classification accuracies of 67% with haptic and 68% visual feedback. One subject achieved as high as 88.8% accuracy in a single session. Stable features selected from sensorimotor rhythms within the 8-12 Hz and 18-26 Hz frequency bands provided the highest accuracies. Only minor interference using haptic stimulation was observed within the 8-30 Hz frequency band. The results indicate no clear differences between learning with haptic or visual feedback. Most subjects felt haptic feedback natural and comfortable. Haptic feedback could thus substitute for visual feedback, and render vision available for other concurrent tasks. Further studies especially with motor-disabled patients in real home environments will be necessary to confirm the results