A brain-computer interface (BCI) enables communication without movement based on brain signals measured with electroencephalography (EEG). BCIs usually rely on one of three types of signals: the P300 and other components of the event-related potential (ERP), steady state visual evoked potential (SSVEP), or event related desynchronization (ERD). Although P300 BCIs were introduced over twenty years ago, the past few years have seen a strong increase in P300 BCI research. This closed-loop BCI approach relies on the P300 and other components of the ERP, based on an oddball paradigm presented to the subject. In this paper, we overview the current status of P300 BCI technology, and then discuss new directions: paradigms for eliciting P300s; signal processing methods; applications; and hybrid BCIs. We conclude that P300 BCIs are quite promising, as several emerging directions have not yet been fully explored and could lead to improvements in bit rate, reliability, usability, and flexibility.

Most brain–computer interfaces (BCIs) rely on one of three types of signals in the electroencephalogram (EEG): P300s, steady-state visually evoked potentials, and event-related desynchronization. EEG is typically recorded non-invasively with electrodes mounted on the human scalp using conductive electrode gel for optimal impedance and data quality. The use of electrode gel entails serious problems that are especially pronounced in real-world settings when experts are not available. Some recent work has introduced dry electrode systems that do not require gel, but often introduce new problems such as comfort and signal quality. The principal goal of this study was to assess a new dry electrode BCI system in a very common task: spelling with a P300 BCI. A total of 23 subjects used a P300 BCI to spell the word “LUCAS” while receiving real-time, closed-loop feedback. The dry system yielded classification accuracies that were similar to those obtained with gel systems. All subjects completed a questionnaire after data recording, and all subjects stated that the dry system was not uncomfortable. This is the first field validation of a dry electrode P300 BCI system, and paves the way for new research and development with EEG recording systems that are much more practical and convenient in field settings than conventional systems.

Introduction: Numerous recent publications have explored Brain Computer Interfaces (BCI) systems as rehabilitation tools to help subacute and chronic stroke patients recover upper extremity movement. Recent work has shown that BCI therapy can lead to better outcomes than conventional therapy. BCI combined with other techniques such as Functional Electrical Stimulation (FES) and Virtual Reality (VR) allows to the user restore the neurological function by inducing the neural plasticity through improved real-time detection of motor imagery (MI) as patients perform therapy tasks. Methods: Fifty-one stroke patients with upper extremity hemiparesis were recruited for this study. All participants performed 25 sessions with the MI BCI and assessment visits to track the functional changes before and after the therapy. Results: The results of this study demonstrated a significant increase in the motor function of the paretic arm assessed by Fugl-Meyer Assessment (FMA-UE), ΔFMA-UE = 4.68 points, P < 0.001, reduction of the spasticity in the wrist and fingers assessed by Modified Ashworth Scale (MAS), ΔMAS-wrist = -0.72 points (SD = 0.83), P < 0.001, ΔMAS-fingers = -0.63 points (SD = 0.82), P < 0.001. Other significant improvements in the grasp ability were detected in the healthy hand. All these functional improvements achieved during the BCI therapy persisted 6 months after the therapy ended. Results also showed that patients with Motor Imagery accuracy (MI) above 80% increase 3.16 points more in the FMA than patients below this threshold (95% CI; [1.47–6.62], P = 0.003). The functional improvement was not related with the stroke severity or with the stroke stage. Conclusion: The BCI treatment used here was effective in promoting long lasting functional improvements in the upper extremity in stroke survivors with severe, moderate and mild impairment. This functional improvement can be explained by improved neuroplasticity in the central nervous system.

Brain-computer interfaces (BCI) are communication systems that allow people to send messages or commands without movement. BCIs rely on different types of signals in the electroencephalogram (EEG), typically P300s, steady-state visually evoked potentials (SSVEP), or event-related desynchronization. Early BCI systems were often evaluated with a selected group of subjects. Also, many articles do not mention data from subjects who performed poorly. These and other factors have made it difficult to estimate how many people could use different BCIs. The present study explored how many subjects could use an SSVEP BCI. We recorded data from 53 subjects while they participated in 1–4 runs that were each 4 min long. During these runs, the subjects focused on one of four LEDs that each flickered at a different frequency. The eight channel EEG data were analyzed with a minimum energy parameter estimation algorithm and classified with linear discriminant analysis into one of the four classes. Online results showed that SSVEP BCIs could provide effective communication for all 53 subjects, resulting in a grand average accuracy of 95.5%. About 96.2% of the subjects reached an accuracy above 80%, and nobody was below 60%. This study showed that SSVEP based BCI systems can reach very high accuracies after only a very short training period. The SSVEP approach worked for all participating subjects, who attained accuracy well above chance level. This is important because it shows that SSVEP BCIs could provide communication for some users when other approaches might not work for them.