Usability study of different platforms to Develop Communication Systems based on P300-Brain-Computer Interface (BCI).

People suffering from neurodegenerative disorders, such as Amyotrophic Lateral Sclerosis (ALS), can eventually present great disabilities. In some cases, these patients lose all possibility to communicate with the external world via common muscular channels, being the only alternative the use of a Brain-Computer Interface (BCI) system, which transforms brain activity into external commands. A P300-speller is a typical Brain-Computer Interface system for communication purpose. In order to facilitate the communication, it is very important to adapt the speller to each patient. The most popular platforms to develop P300 speller are BCI2000, OpenVibe and UMA-BCI Speller. The goal of this study was to evaluate the usability of the three proposed platforms in terms of effectiveness, efficiency and satisfaction. To this end, three participants had to configure a specific speller layout using the 3 platforms. The obtained results indicated that the UMA-BCI Speller platform presented the highest level of usability, following by the BCI2000 and finally, the OpenVibe platform. In this sense, the UMA-BCI Speller seems to be an easy application to use, providing many options and allowing to configure any speller layout in an easy way.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

P300-Based BCI Performance Prediction through Examination of Paradigm Manipulations and Principal Components Analysis.

Severe neuromuscular disorders can produce locked-in syndrome (LIS), a loss of nearly all voluntary muscle control. A brain-computer interface (BCI) using the P300 event-related potential provides communication that does not depend on neuromuscular activity and can be useful for those with LIS. Currently, there is no way of determining the effectiveness of P300-based BCIs without testing a person\u27s performance multiple times. Additionally, P300 responses in BCI tasks may not resemble the typical P300 response. I sought to clarify the relationship between the P300 response and BCI task parameters and examine the possibility of a predictive relationship between traditional oddball tasks and BCI performance. Both waveform and component analysis have revealed several task-dependent aspects of brain activity that show significant correlation with the user\u27s performance. These components may provide a fast and reliable metric to indicate whether the BCI system will work for a given individual

Design, implementation and evaluation of a real-time P300-based brain-computer interface system

We present a new end-to-end brain-computer interface system based on electroencephalography (EEG). Our system exploits the P300 signal in the brain, a positive deflection in event-related potentials, caused by rare events. P300 can be used for various tasks, perhaps the most well-known being a spelling device. We have designed a flexible visual stimulus mechanism that can be adapted to user preferences and developed and implemented EEG signal processing, learning and classification algorithms. Our classifier is based on Bayes linear discriminant analysis, in which we have explored various choices and improvements. We have designed data collection experiments for offline and online decision-making and have proposed modifications in the stimulus and decision-making procedure to increase online efficiency. We have evaluated the performance of our system on 8 healthy subjects on a spelling task and have observed that our system achieves higher average speed than state-of-the-art systems reported in the literature for a given classification accuracy

Manipulating Paradigm and Attention via a Mindfulness Meditation Training Program Improves P300-Based BCI.

To date, only one study has situationally bolstered attentional resources in an effort to improve P300-BCI performance. The current study implements a 4-week Mindfulness Meditation Training Program (MMTP) as a nonmedicinal means to increase concentrative attention and to reduce lapses of attention; MMTP is expected to improve P300-BCI performance by enhancing attentional resources and reducing distractibility. A second aim is to test the efficacy of the checkerboard paradigm (CBP) against the standard row-column paradigm (RCP). Online results show that MMTP had greater accuracies than CTRL and that CBP outperformed the RCP. MMTP participants provided greater amplitude positive target responses, but these differences were not statistically significant. CBP had greater positive amplitude peaks and negative peaks than RCP. The discussion focuses on potential benefits of MMTP for P300-based BCIs, provides further support for the construct validity of mindfulness, and addresses future directions of the translational applicability of MMTP to in-home settings

Hybrid Brain-Computer Interface Systems: Approaches, Features, and Trends

Brain-computer interface (BCI) is an emerging field, and an increasing number of BCI research projects are being carried globally to interface computer with human using EEG for useful operations in both healthy and locked persons. Although several methods have been used to enhance the BCI performance in terms of signal processing, noise reduction, accuracy, information transfer rate, and user acceptability, the effective BCI system is still in the verge of development. So far, various modifications on single BCI systems as well as hybrid are done and the hybrid BCIs have shown increased but insufficient performance. Therefore, more efficient hybrid BCI models are still under the investigation by different research groups. In this review chapter, single BCI systems are briefly discussed and more detail discussions on hybrid BCIs, their modifications, operations, and performances with comparisons in terms of signal processing approaches, applications, limitations, and future scopes are presented