EEG Single-Trial Classification of Visual, Auditive and Vibratory Feedback Potentials in Brain-Computer Interfaces

Feedback stimuli are fundamental components in Brain-Computer Interfaces. It is known that the presentation of feedback stimuli elicits certain brain potentials that can be measured and classified. As stimuli can be given through different sensory modalities, it is important to understand the effects of different types of feedback on brain responses and their impact on classification. This paper presents a protocol used to obtain brain potentials elicited by visual, auditive or vibrotactile feedback stimuli. Experiments were carried out with five different subjects for each modality. Four different single-trial classification strategies were compared, according to the information used to train the classifier, achieving a classification rate of approximately 80% for each modality

Single-Trial Classification of Feedback Potentials within Neurofeedback Training with an EEG Brain-Computer Interface

Neurofeedback therapies are an emerging technique used to treat neuropsychological disorders and to enhance cognitive performance. The feedback stimuli presented during the therapy are a key factor, serving as guidance throughout the entire learning process of the brain rhythms. Online decoding of these stimuli could be of great value to measure the compliance and adherence of the subject to the training. This paper describes the modeling and classification of performance feedback potentials with a Brain-Computer Interface (BCI), under a real neurofeedback training with five subjects. LDA and SVM classification techniques are compared and are both able to provide an average performance of approximately 80%

Allelic overload and its clinical modifier effect in Bardet-Biedl syndrome

Bardet–Biedl syndrome (BBS) is an autosomal recessive ciliopathy characterized by extensive inter- and intra-familial variability, in which oligogenic interactions have been also reported. Our main goal is to elucidate the role of mutational load in the clinical variability of BBS. A cohort of 99 patients from 77 different families with biallelic pathogenic variants in a BBS-associated gene was retrospectively recruited. Human Phenotype Ontology terms were used in the annotation of clinical symptoms. The mutational load in 39 BBS-related genes was studied in index cases using different molecular and next-generation sequencing (NGS) approaches. Candidate allele combinations were analysed using the in silico tools ORVAL and DiGePred. After clinical annotation, 76 out of the 99 cases a priori fulfilled established criteria for diagnosis of BBS or BBS-like. BBS1 alleles, found in 42% of families, were the most represented in our cohort. An increased mutational load was excluded in 41% of the index cases (22/54). Oligogenic inheritance was suspected in 52% of the screened families (23/45), being 40 tested by means of NGS data and 5 only by traditional methods. Together, ORVAL and DiGePred platforms predicted an oligogenic effect in 44% of the triallelic families (10/23). Intrafamilial variable severity could be clinically confirmed in six of the families. Our findings show that the presence of more than two alleles in BBSassociated genes correlated in six families with a more severe phenotype and associated with specific findings, highlighting the role of the mutational load in the management of BBS casesInstituto de Salud Carlos III | Ref. PI15/00049Instituto de Salud Carlos III | Ref. PI16/00425Instituto de Salud Carlos III | Ref. PI19/00321Instituto de Salud Carlos III | Ref. PI19/00332CIBERER | Ref. 07/06/0036IIS-FJD BioBank | Ref. PT13/0010/0012Comunidad de Madrid | Ref. B2017/BMD-3721Xunta de Galicia | Ref. ED431G-2019/06Xunta de Galicia | Ref. ED431C-2018/54ISCIII | Ref. FI17/00192Ministerio de Educación, Cultura y Deporte | Ref. FPU 19/00175ISCIII | Ref. CP16/0011

Control of an ambulatory exoskeleton with a brain-machine interface for spinal cord injury gait rehabilitation

The closed-loop control of rehabilitative technologies by neural commands has shown a great potential to improve motor recovery in patients suffering from paralysis. Brain-machine interfaces (BMI) can be used as a natural control method for such technologies. BMI provides a continuous association between the brain activity and peripheral stimulation, with the potential to induce plastic changes in the nervous system. Paraplegic patients, and especially the ones with incomplete injuries, constitute a potential target population to be rehabilitated with brain-controlled robotic systems, as they may improve their gait function after the reinforcement of their spared intact neural pathways. This paper proposes a closed-loop BMI system to control an ambulatory exoskeleton-without any weight or balance support-for gait rehabilitation of incomplete spinal cord injury (SCI) patients. The integrated system was validated with three healthy subjects, and its viability in a clinical scenario was tested with four SCI patients. Using a cue-guided paradigm, the electroencephalographic signals of the subjects were used to decode their gait intention and to trigger the movements of the exoskeleton. We designed a protocol with a special emphasis on safety, as patients with poor balance were required to stand and walk. We continuously monitored their fatigue and exertion level, and conducted usability and user-satisfaction tests after the experiments. The results show that, for the three healthy subjects, 84.44 ± 14.56% of the trials were correctly decoded. Three out of four patients performed at least one successful BMI session, with an average performance of 77.6 1 ± 14.72%. The shared control strategy implemented (i.e., the exoskeleton could only move during specific periods of time) was effective in preventing unexpected movements during periods in which patients were asked to relax. On average, 55.22 ± 16.69% and 40.45 ± 16.98% of the trials (for healthy subjects and patients, respectively) would have suffered from unexpected activations (i.e., false positives) without the proposed control strategy. All the patients showed low exertion and fatigue levels during the performance of the experiments. This paper constitutes a proof-of-concept study to validate the feasibility of a BMI to control an ambulatory exoskeleton by patients with incomplete paraplegia (i.e., patients with good prognosis for gait rehabilitation)

Real-time recognition of feedback error-related potentials during a time-estimation task

Abstract-Feedback error-related potentials are a promising brain process in the field of rehabilitation since they are related to human learning. Due to the fact that many therapeutic strategies rely on the presentation of feedback stimuli, potentials generated by these stimuli could be used to ameliorate the patient's progress. In this paper we propose a method that can identify, in real-time, feedback evoked potentials in a time-estimation task. We have tested our system with five participants in two different days with a separation of three weeks between them, achieving a mean single-trial detection performance of 71.62% for real-time recognition, and 78.08% in offline classification. Additionally, an analysis of the stability of the signal between the two days is performed, suggesting that the feedback responses are stable enough to be used without the needing of training again the user

Continuous decoding of movement intention of upper limb self-initiated analytic movements from pre-movement EEG correlates

 Background: Brain-machine interfaces (BMI) have recently been integrated within motor rehabilitation therapies by actively involving the central nervous system (CNS) within the exercises. For instance, the online decoding of intention of motion of a limb from pre-movement EEG correlates is being used to convert passive rehabilitation strategies into active ones mediated by robotics. As early stages of upper limb motor rehabilitation usually focus on analytic single-joint mobilizations, this paper investigates the feasibility of building BMI decoders for these specific types of movements. Methods: Two different experiments were performed within this study. For the first one, six healthy subjects performed seven self-initiated upper-limb analytic movements, involving from proximal to distal articulations. For the second experiment, three spinal cord injury patients performed two of the previously studied movements with their healthy elbow and paralyzed wrist. In both cases EEG neural correlates such as the event-related desynchronization (ERD) and movement related cortical potentials (MRCP) were analyzed, as well as the accuracies of continuous decoders built using the pre-movement features of these correlates (i.e., the intention of motion was decoded before movement onset). Results: The studied movements could be decoded in both healthy subjects and patients. For healthy subjects there were significant differences in the EEG correlates and decoding accuracies, dependent on the moving joint. Percentages of correctly anticipated trials ranged from 75% to 40% (with chance level being around 20%), with better performances for proximal than for distal movements. For the movements studied for the SCI patients the accuracies were similar to the ones of the healthy subjects. Conclusions: This paper shows how it is possible to build continuous decoders to detect movement intention from EEG correlates for seven different upper-limb analytic movements. Furthermore we report differences in accuracies among movements, which might have an impact on the design of the rehabilitation technologies that will integrate this new type of information. The applicability of the decoders was shown in a clinical population, with similar performances between healthy subjects and patients