Critical issues in state-of-the-art brain–computer interface signal processing

This paper reviews several critical issues facing signal processing for brain–computer interfaces (BCIs) and suggests several recent approaches that should be further examined. The topics were selected based on discussions held during the 4th International BCI Meeting at a workshop organized to review and evaluate the current state of, and issues relevant to, feature extraction and translation of field potentials for BCIs. The topics presented in this paper include the relationship between electroencephalography and electrocorticography, novel features for performance prediction, time-embedded signal representations, phase information, signal non-stationarity, and unsupervised adaptation

Detecting Intentional Mental Transitions in an Asynchronous BCI

The inclusion of mental tasks transitions detection (MTTD) has proven a useful tool in guiding the transduction process of a BCI working under an asynchronous protocol. MTTD allows for the extraction of the signal's contextual information in order to infer the user's intentionality at a given moment and thus correcting possible classification errors. Despite the good results shown, the algorithm previously proposed \cite{1} does not show good behavior in contexts where the user gets online feedback. The algorithm that we propose in this paper, like its antecessor, is based on canonical variates transformation (CVT) and on distance-based discriminant analysis (DBDA), but it has a new transitions detector based on Kalman filtering. In addition, it includes a classifier supervisor based on heuristics rules that exploit transition detection as well as inconsistencies between subject's mental intention and the associated EEG. These heuristic rules lead to significant improvements of the BCI in terms of both classification accuracy and channel capacity, adapting itself to the user's needs

Non-Invasive Brain-Actuated Interaction

The promise of Brain-Computer Interfaces (BCI) technology is to augment human capabilities by enabling interaction with computers through a conscious and spontaneous modulation of the brainwaves after a short training period. Indeed, by analyzing brain electrical activity online, several groups have designed brain-actuated devices that provide alternative channels for communication, entertainment and control. Thus, a person can write messages using a virtual keyboard on a computer screen and also browse the internet. Alternatively, subjects can operate simple computer games, or brain games, and interact with educational software. Work with humans has shown that it is possible for them to move a cursor and even to drive a wheelchair. This paper briefly reviews the field of BCI, with a focus on non-invasive systems based on electroencephalogram (EEG) signals. It also describes three brain-actuated devices we have developed: a virtual keyboard, a brain game, and a wheelchair. Finally, it shortly discusses current research directions we are pursuing in order to improve the performance and robustness of our BCI system, especially for real-time control of brain-actuated robots

Differentiation of Epoxide Enantiomers in the Confined Spaces of an Homochiral Cu(II) MOF by Kinetic Resolution

TAMOF-1, a homochiral metal-organic framework (MOF) constructed from an amino acid derivative and Cu(II), was investigated as a heterogeneous catalyst in kinetic resolutions involving the ring opening of styrene oxide with a set of anilines. The branched products generated from the ring opening of styrene oxide with anilines and the unreacted epoxide were obtained with moderately high enantiomeric excesses. The linear product arising from the attack on the non-benzylic position of styrene oxide underwent a second kinetic resolution by reacting with the epoxide, resulting in an amplification of its final enantiomeric excess and a concomitant formation of an array of isomeric aminodiols. Computational studies confirmed the experimental results, providing a deep understanding of the whole process involving the two successive kinetic resolutions. Furthermore, TAMOF-1 activity was conserved after several catalytic cycles. The ring opening of a mesoepoxide with aniline catalyzed by TAMOF-1 was also studied and moderate enantioselectivities were obtained

Separation of Volatile Organic Compounds in TAMOF-1

Separation of volatile organic compounds is one of the most studied processes in industry. TAMOF-1 is a homochiral metal-organic framework with a crystalline network of interconnected 1 nm channels and has high thermal and chemical stability. Thanks to these features, it can resolve racemic mixtures of chiral drugs as a chiral stationary phase in chromatography. Interestingly, the particular shape and size of its channels, along with the presence of metallic centers and functional groups, allow establishing weak but significant interactions with guest molecules. This opens interesting possibilities not only to resolve racemates but also to separate other organic mixtures, such as saturated/unsaturated and/or linear/branched molecules. In search of these applications, we have studied the separation of volatile organic compounds in TAMOF-1. Monte Carlo simulations in the grand-canonical ensemble have been carried out to evaluate the separation of the selected molecules. Our results predict that TAMOF-1 is able to separate xylene isomers, hexane isomers, and benzene-cyclohexane mixtures. Experimental breakthrough analysis in the gas phase and also in the liquid phase confirms these predictions. Beds of TAMOF-1 are able to recognize the substitution in xylenes and the branching in hexanes, yielding excellent separation and reproducibility, thanks to the chemical and mechanical features of this material.Universidad Pablo de Olavide. Departamento de Sistemas Físicos, Químicos y Naturale

Critical issues in state-of-the-art brain-computer interface signal processing

Abstract This paper reviews several critical issues facing signal processing for brain-computer interfaces (BCIs) and suggests several recent approaches that should be further examined. The topics were selected based on discussions held during the 4th International BCI Meeting at a workshop organized to review and evaluate the current state of, and issues relevant to, feature extraction and translation of field potentials for BCIs. The topics presented in this paper include the relationship between electroencephalography and electrocorticography, novel features for performance prediction, time-embedded signal representations, phase information, signal non-stationarity, and unsupervised adaptation

A Brain-Actuated Wheelchair: Asynchronous and Non-Invasive Brain-Computer Interfaces for Continuous Control of Robots

Objective: To assess the feasibility and robustness of an asynchronous and non-invasive EEG-based Brain-Computer Interface (BCI) for continuous mental control of a wheelchair. Methods: In experiment 1 two subjects were asked to mentally drive both a real and a simulated wheelchair from a starting point to a goal along a pre-specified path. Here we only report experiments with the simulated wheelchair for which we have extensive data in a complex environment that allows a sound analysis. Each subject participated in 5 experimental sessions, each consisting of 10 trials. The time elapsed between two consecutive experimental sessions was variable (from one hour to two months) to assess the system robustness over time. The pre-specified path was divided in 7 stretches to assess the system robustness in different contexts. To further assess the performance of the brain-actuated wheelchair, subject 1 participated in a second experiment consisting of 10 trials where he was asked to drive the simulated wheelchair following 10 different complex and random paths never tried before. Results: In experiment 1 the two subjects were able to reach 100% (subject 1) and 80% (subject 2) of the final goals along the pre-specified trajectory in their best sessions. Different performances were obtained over time and path stretches, what indicates that performance is time and context dependent. In experiment 2, subject 1 was able to reach the final goal in 80% of the trials. Conclusions: The results show that subjects can rapidly master our asynchronous EEG-based BCI to control a wheelchair. Also, they can autonomously operate the BCI over long periods of time without the need for adaptive algorithms externally tuned by a human operator to minimize the impact of EEG non-stationarities. This is possible because of two key components: first, the inclusion of a shared control system between the BCI system and the intelligent simulated wheelchair; second, the selection of stable user-specific EEG features that maximize the separability between the mental tasks. Significance: These results show the feasibility of continuously controlling complex robotics devices using an asynchronous and non-invasive BCI