UMA-BCI Speller: An easily configurable P300 speller tool for end users.

BACKGROUND AND OBJECTIVE: Some neurodegenerative conditions can severely limit patients' capability to communicate because of the loss of muscular control. Brain-computer interfaces may help in the restoration of communication with these patients, bypassing the muscular activity, so that brain signals can be directly interpreted by a computer. There are many studies regarding brain-controlled spellers; however, these systems do not usually leap out of the lab because of technical and economic requirements. As a consequence, the potential end users do not benefit from these scientific advances in their daily life. The objective of this paper is to present a novel brain-controlled speller designed to be used by patients due to its versatility and ease of use. METHODS: The brain-computer interface research group of the University of Málaga (UMA-BCI) has developed a speller application based on the well-known P300 potential which can be easily installed, configured and used. The application supports the common P300 paradigms: the Row-Column Paradigm and the Rapid Serial Visual Presentation Paradigm. The inner core of the application is implemented with a widely used and studied platform, BCI2000, which ensures its reliability and allows other researchers to apply modifications at will in order to test new features. Ten naïve volunteers carried out exercises using the application and completed usability tests for evaluation purposes. RESULTS: New subjects using the application managed to set up and use the proposed speller in less than an hour. The positive results of the evaluation through the usability tests support this application's ease of use. CONCLUSIONS: A new brain-controlled spelling tool has been presented whose aim is to be used by severely paralyzed patients in their daily lives, as well as by researchers to test new spelling features.

Brain-computer interface: proposal of a shaping-based training

INTRODUCTION: Persons affected by certain motor disabilities such as amyotrophic lateral sclerosis can evolve with important motor and speech difficulties in communication. A BCI (Brain Computer Interface) is a system that allows interaction between the human brain and a computer, permitting the user to control a communication channel through his or her brain activity. It is based on the analysis and processing of electroencephalographic (EEG) signals to generate control commands. The present study focuses on the subjects' capability to improve the way they learn to control a BCI system. METHODS: Two training procedures were compared: standard and progressive shaping response. Six volunteers participated in a reversal single-subject ABAC design. RESULTS: The study showed that both procedures are equally effective in producing a differential responding in the EEG signals, with no significant differences between them. Nevertheless, there were significant differences when distinguishing two neuronal responses (relax state and hand-movement imagination). Also, in the analysis of individual signals, an adaptive process for the shaping process and a lower error rate in the idle response appeared. CONCLUSION: Both proposed training procedures, standard and progressive shaping, are equally effective to achieve training of differential responses (imagination of hand/relax) in the interaction with a BCI.

A two-class self-paced BCI to control a robot in four directions.

In this work, an electroencephalographic analysis-based, self-paced (asynchronous) brain-computer interface (BCI) is proposed to control a mobile robot using four different navigation commands: turn right, turn left, move forward and move back. In order to reduce the probability of misclassification, the BCI is to be controlled with only two mental tasks (relaxed state versus imagination of right hand movements), using an audio-cued interface. Four healthy subjects participated in the experiment. After two sessions controlling a simulated robot in a virtual environment (which allowed the user to become familiar with the interface), three subjects successfully moved the robot in a real environment. The obtained results show that the proposed interface enables control over the robot, even for subjects with low BCI performance.