This work presents the design, implementation, and evaluation of a P300-based brain-machine interface (BMI) developed to control a robotic hand-orthosis. The purpose of this system is to assist patients with amyotrophic lateral sclerosis (ALS) who cannot open and close their hands by themselves. The user of this interface can select one of six targets, which represent the flexion-extension of one finger independently or the movement of the five fingers simultaneously. We tested offline and online our BMI on eighteen healthy subjects (HS) and eight ALS patients. In the offline test, we used the calibration data of each participant recorded in the experimental sessions to estimate the accuracy of the BMI to classify correctly single epochs as target or non-target trials. On average, the system accuracy was 78.7% for target epochs and 85.7% for non-target trials. Additionally, we observed significant P300 responses in the calibration recordings of all the participants, including the ALS patients. For the BMI online test, each subject performed from 6 to 36 attempts of target selections using the interface. In this case, around 46% of the participants obtained 100% of accuracy, and the average online accuracy was 89.83%. The maximum information transfer rate (ITR) observed in the experiments was 52.83 bit/min, whereas that the average ITR was 18.13 bit/min. The contributions of this work are the following. First, we report the development and evaluation of a mind-controlled robotic hand-orthosis for patients with ALS. To our knowledge, this BMI is one of the first P300-based assistive robotic devices with multiple targets evaluated on people with ALS. Second, we provide a database with calibration data and online EEG recordings obtained in the evaluation of our BMI. This data is useful to develop and compare other BMI systems and test the processing pipelines of similar applications.

Motor imagery (MI)-based brain-computer interface (BCI) systems have shown promising advances for lower limb motor rehabilitation. The purpose of this study was to develop an MI-based BCI for the actions of standing and sitting. Thirty-two healthy subjects participated in the study using 17 active EEG electrodes. We used a combination of the filter bank common spatial pattern (FBCSP) method and the regularized linear discriminant analysis (RLDA) technique for decoding EEG rhythms offline and online during motor imagery for standing and sitting. The offline analysis indicated the classification of motor imagery and idle state provided a mean accuracy of 88.51 ± 1.43% and 85.29 ± 1.83% for the sit-to-stand and stand-to-sit transitions, respectively. The mean accuracies of the sit-to-stand and stand-to-sit online experiments were 94.69 ± 1.29% and 96.56 ± 0.83%, respectively. From these results, we believe that the MI-based BCI may be useful to future brain-controlled standing systems.

This work presents the design, implementation, and feasibility evaluation of a Motor Imagery (MI) based Brain-Computer Interface (BCI) developed to control a Functional Electrical Stimulation (FES) device. The aim of this system is to assist the upper limb motor recovery of patients with spinal cord injury (SCI). With this BCI-controlled FES system, the user performs open and close MI with either the left or right hand, which if detected is used to provide visual feedback and electroestimulation to muscles in the forearm to perform the corresponding grasping movement. The system was evaluated with seven healthy subjects (HS group) and two SCI patients (SC group) in several experimental sessions across different days. Each experimental session consisted of a training routine devoted to collect calibration EEG data to train the BCI machine learning model, and of a validation routine devoted to validate system in online operation. The online system validation showed an accuracy of the recognition of the MI task that ranged between 78% and 81% for HS participants and between 63% and 93% for SCI participants. Additionally, the time taken by the BCI system to trigger the FES device ranged between 7.05 and 7.29 s for HS participants and between 8.43 s and 13.91 s for SCI participants. Finally, significant negative correlations were observed ( $r=-0.418$ , $p=0.024$ and $r=-0.437$ , $p=0.018$ for left and right hand MI conditions, respectively) between the online BCI performance with a quantitative EEG parameter based on event-related desynchronization/synchronization analysis. The results of this work indicate the feasibility of the proposed BCI coupled to a FES device to be used for SCI patients with a moderate level of disability and provides evidence of the functionality of the proposed BCI system in a motor rehabilitation context.

Currently, one of the challenges in EEG-based brain-computer interfaces (BCI) for neurorehabilitation is the recognition of the intention to perform different movements from the same limb. This would allow finer control of neurorehabilitation and motor recovery devices by end-users. To address this issue, we assess the feasibility of recognizing two rehabilitative right upper-limb movements from pre-movement EEG signals. These rehabilitative movements were performed self-selected and self-initiated by the users using a motor rehabilitation robotic device. This work proposes anticipatory detection scenarios that discriminate EEG signals corresponding to non-movement state and movement intentions of two same-limb movements. The studied movements were discriminated above the empirical chance levels for all proposed detection scenarios. Percentages of correctly anticipated trials ranged from 64.3% to 77.0%, and the detection times ranged from 620 to 300 ms prior to movement initiation. The results of these studies indicate that it is possible to detect the intention to perform two different movements of the same upper limb and non-movement state. Based on these results, the decoding of the movement intention could potentially be used to develop more natural and intuitive robot-assisted neurorehabilitation therapies.

Brain-Computer Interfaces (BCI) based on Steady-State Visually Evoked Potentials (SSVEP) have great potential for use in communication applications because of their relatively simple assembly and in some cases the possibility of bypassing the time-consuming training stage. However, among multiple factors, the efficient performance of this technology is highly dependent on the stimulation paradigm applied in combination with the SSVEP detection algorithm employed. This paper proposes the performance assessment of the classification of target events with respect to non-target events by applying four types of visual paradigms, rectangular modulated On-Off (OOR), sinusoidal modulated On-Off (OOS), rectangular modulated Checkerboard (CBR) and sinusoidal modulated Checkerboard (CBS), with three types of SSVEP detection methods, Canonical Correlation Analysis (CCA), Filter-Bank CCA (FBCCA) and Minimum Energy Combination (MEC). We set up an experimental protocol in which the four types of visual stimuli were presented randomly to twenty-seven participants and after acquiring their electroencephalographic responses to five stimulation frequencies ($8.57, 10.909, 15, 20$, and $24$ Hz), the three detection methods were applied to the collected data. The results are conclusive, obtaining the best performance with the combination of either OOR or OOS visual stimulus and the FBCCA as a detection method, however, this finding contrasts with the opinion of almost half of the participants in terms of visual comfort, where the 51.9\% of the subjects felt more comfortable and focused with CBR or CBS stimulation. Finally, the EEG recordings correspond to the SSVEP response of 27 subjects to four visual paradigms when selecting five items on a screen, which is useful in BCI navigation applications. The dataset is available to anyone interested in studying and evaluating signal processing and machine-learning algorithms for SSVEP-BCI systems.