Advanced Brain-Computer Interface (BCI) paradigms aim to solve some problems as BCI illiteracy and unfamiliarity of the subjects to be able to control their elicited motor imagery (MI) successfully, hence improving training time and performance of BCI systems. This work evaluates the effect and performance of an Implicit BCI supported by the Gaze Monitoring (IBCI-GM) paradigm for virtual rehabilitation therapy of patients suffering from partial or total paralysis of their upper limbs; this paradigm also was compared with alternative forms of advanced BCI methods such as Virtual Reality-based BCI (VR-BCI) with a head-mounted display (HMD) and a computer screen (CS). Eight subjects participated in the experiments; four subjects tested the VR-BCI with a CS, and the rest of them tested both BCI advanced methods (IBCI-GM and VR-BCI with an HMD). The subjects were asked to control a virtual ar...

In this work, we present a novel EEG-based Linguistic BCI, which uses the four phonemic structures "BA", "FO", "LE", and "RY" as covert speech task classes. Six neurologically healthy volunteers with the age range of 19-37 participated in this experiment. Participants were asked to covertly speak a phonemic structure when they heard an auditory cue. EEG was recorded with 64 electrodes at 2048 samples/s. The duration of each trial is 312ms starting with the cue. The BCI was trained using a mixed randomized recording run containing 15 trials per class. The BCI is tested by playing a simple game of "Wack a mole" containing 5 trials per class presented in random order. The average classification accuracy for the 6 users is 82.5%. The most valuable features emerge after Auditory cue recognition (~100ms post onset), and within the 70-128 Hz frequency range. The most significant identified brai...

Motor Imagery based BCIs (MI-BCIs) allow the control of devices and communication by imagining different mental tasks. Despite many years of research, BCIs are still not the most accurate systems to control applications, due to two main factors: signal processing with classification, and users. It is admitted that BCI control involves certain characteristics and abilities in its users for optimal results. In this study, spatial abilities are evaluated in relation to MI-BCI control regarding flexion and extension mental tasks. Results show considerable correlation (r=0.49) between block design test (visual motor execution and spatial visualization) and extension-rest tasks. Additionally, rotation test (mental rotation task) presents significant correlation (r=0.56) to flexion-rest tasks.

The brain‐computer interface (BCI) uses electrical signals from the brain and uses them as information to control an external device and has the potential to stimulate neuroplasticity in motor impairment. Objective. Implement an immersive virtual reality and BCI system for upper limb rehabilitation and test its safety. Methods: 34 healthy subjects participated. We used an EEG of 16 channels placed on the scalp corresponding to the motor and pre‐motor area, an amplifier (g.USBAmp) and a signal classification method (ANFIS classifier), to detect mu and beta signals related to the intention of movement and used to control an immersive virtual reality representation of an upper limb; and perform 2 movements: flexion and extension, according to the brain signals detected. In addition, the classification percentages and their association with spatial abilities of participants were measured...