Pietro Aricò

A covert attention P300-based brain–computer interface: Geospell

The Farwell and Donchin P300 speller interface is one of the most widely used brain–computer interface (BCI) paradigms for writing text. Recent studies have shown that the recognition accuracy of the P300 speller decreases significantly when eye movement is impaired. This report introduces the GeoSpell interface (Geometric Speller), which implements a stimulation framework for a P300-based BCI that has been optimised for operation in covert visual attention. We compared the Geospell with the P300 speller interface under overt attention conditions with regard to effectiveness, efficiency and user satisfaction. Ten healthy subjects participated in the study. The performance of the GeoSpell interface in covert attention was comparable with that of the P300 speller in overt attention. As expected, the effectiveness of the spelling decreased with the new interface in covert attention. The NASA task load index (TLX) for workload assessment did not differ significantly between the two modalities. Practitioner Summary: This study introduces and evaluates a gaze-independent, P300-based brain–computer interface, the efficacy and user satisfaction of which were comparable with those off the classical P300 speller. Despite a decrease in effectiveness due to the use of covert attention, the performance of the GeoSpell far exceeded the threshold of accuracy with regard to effective spelling.

A comparison of classification techniques for a gaze-independent P300-based brain–computer interface

This off-line study aims to assess the performance of five classifiers commonly used in the brain-computer interface (BCI) community, when applied to a gaze-independent P300-based BCI. In particular, we compared the results of four linear classifiers and one nonlinear: Fishers linear discriminant analysis (LDA), stepwise linear discriminant analysis (SWLDA), Bayesian linear discriminant analysis (BLDA), linear support vector machine (LSVM) and Gaussian supported vector machine (GSVM). Moreover, different values for the decimation of the training dataset were tested. The results were evaluated both in terms of accuracy and written symbol rate with the data of 19 healthy subjects. No significant differences among the considered classifiers were found. The optimal decimation factor spanned a range from 3 to 24 (12 to 94 ms long bins). Nevertheless, performance on individually optimized classification parameters is not significantly different from a classification with general parameters (i.e. using an LDA classifier, about 48 ms long bins).

Quantitative Assessment of the Training Improvement in a Motor-Cognitive Task by Using EEG, ECG and EOG Signals

Generally, the training evaluation methods consist in experts supervision and qualitative check of the operator’s skills improvement by asking them to perform specific tasks and by verifying the final performance. The aim of this work is to find out if it is possible to obtain quantitative information about the degree of the learning process throughout the training period by analyzing neuro-physiological signals, such as the electroencephalogram, the electrocardiogram and the electrooculogram. In fact, it is well known that such signals correlate with a variety of cognitive processes, e.g. attention, information processing, and working memory. A group of 10 subjects have been asked to train daily with the NASA multi-attribute-task-battery. During such training period the neuro-physiological, behavioral and subjective data have been collected. In particular, the neuro-physiological signals have been recorded on the first (T1), on the third (T3) and on the last training day (T5), while the behavioral and subjective data have been collected every day. Finally, all these data have been compared for a complete overview of the learning process and its relations with the neuro-physiological parameters. It has been shown how the integration of brain activity, in the theta and alpha frequency bands, with the autonomic parameters of heart rate and eyeblink rate could be used as metric for the evaluation of the learning progress, as well as the final training level reached by the subjects, in terms of request of cognitive resources.

Asynchronous gaze-independent event-related potential-based brain-computer interface

OBJECTIVE In this study a gaze independent event related potential (ERP)-based brain computer interface (BCI) for communication purpose was combined with an asynchronous classifier endowed with dynamical stopping feature. The aim was to evaluate if and how the performance of such asynchronous system could be negatively affected in terms of communication efficiency and robustness to false positives during the intentional no-control state. MATERIAL AND METHODS The proposed system was validated with the participation of 9 healthy subjects. A comparison was performed between asynchronous and synchronous classification technique outputs while users were controlling the same gaze independent BCI interface. The performance of both classification techniques were assessed both off-line and on-line by means of the efficiency metric introduced by Bianchi et al. (2007). This latter metric allows to set a different misclassification cost for wrong classifications and abstentions. Robustness was evaluated as the rate of false positives occurring during voluntary no-control states. RESULTS The asynchronous classifier did not exhibited significantly higher accuracy or lower error rate with respect to the synchronous classifier (accuracy: 74.66% versus 87.96%, error rate: 7.11% versus 12.04% respectively). However, the on-line and off-line analysis revealed that the communication efficiency was significantly improved (p<.05) with the asynchronous classification modality as compared with the synchronous. Furthermore, the asynchronous classifier proved to be robust to false positives during intentional no-control state which occur during the ongoing visual stimulation (less than 1 false positive every 6min). CONCLUSION As such, the proposed ERP-BCI system which combines an asynchronous classifier with a gaze independent interface is a promising solution to be further explored in order to increase the general usability of ERP-based BCI systems designed for severely disabled people with an impairment of the voluntary control of eye movements. In fact, the asynchronous classifier can improve communication efficiency automatically adapting the number of stimulus repetitions to the current users state and suspending the control if he/she does not intend to select an item.

Influence of P300 latency jitter on event related potential-based brain-computer interface performance

OBJECTIVE Several ERP-based brain-computer interfaces (BCIs) that can be controlled even without eye movements (covert attention) have been recently proposed. However, when compared to similar systems based on overt attention, they displayed significantly lower accuracy. In the current interpretation, this is ascribed to the absence of the contribution of short-latency visual evoked potentials (VEPs) in the tasks performed in the covert attention modality. This study aims to investigate if this decrement (i) is fully explained by the lack of VEP contribution to the classification accuracy; (ii) correlates with lower temporal stability of the single-trial P300 potentials elicited in the covert attention modality. APPROACH We evaluated the latency jitter of P300 evoked potentials in three BCI interfaces exploiting either overt or covert attention modalities in 20 healthy subjects. The effect of attention modality on the P300 jitter, and the relative contribution of VEPs and P300 jitter to the classification accuracy have been analyzed. MAIN RESULTS The P300 jitter is higher when the BCI is controlled in covert attention. Classification accuracy negatively correlates with jitter. Even disregarding short-latency VEPs, overt-attention BCI yields better accuracy than covert. When the latency jitter is compensated offline, the difference between accuracies is not significant. SIGNIFICANCE The lower temporal stability of the P300 evoked potential generated during the tasks performed in covert attention modality should be regarded as the main contributing explanation of lower accuracy of covert-attention ERP-based BCIs.

Towards a multimodal bioelectrical framework for the online mental workload evaluation.

In this study, a framework able to classify online different levels of mental workload induced during a simulated flight by using the combination of the Electroencephalogram (EEG) and the Heart Rate (HR) biosignals has been proposed. Ten healthy subjects were involved in the experimental protocol, performing the NASA - Multi Attribute Task Battery (MATB) over three different difficulty levels in order to simulate three classic showcases in a flight scene (cruise flight phase, flight level maintaining, and emergencies). The analyses showed that the proposed system is able to estimate online the mental workload of the subjects over the three different conditions reaching a high discriminability (p<;.05). In addition, it has been found that the classification parameters remained stable within a week, without recalibrating the system with new parameters.

Advanced brain computer interface for communication and control

The brain computer interface (BCI) technology allows a direct connection between brain and computer without any muscular activity required, and thus it offers a unique opportunity to enhance and/or to restore communication and actions into external word in people with severe motor disability. Here, we present the framework of the current research progresses regarding noninvasive EEG-based BCI applications specifically devoted to interact with the environment and other software. The P300 potentials recorded from the scalp represent a suitable BCI signal control for applications like environmental control. Here we present a set of findings that confirm the feasibility of a real domotic environmental control operated via P300-based BCI and a novelty interface approach to evoke the P300 signal.

Avionic technology testing by using a cognitive neurometric index: A study with professional helicopter pilots

In this study, we investigated the possibility to evaluate the impact of different avionic technologies on the mental workload of helicopters pilots by measuring their brain activity with the EEG during a series of simulated missions carried out at AgustaWestland facilities in Yeovil (UK). The tested avionic technologies were: i) Head-Up Display (HUD); ii) Head-Mounted Display (HMD); iii) Full Conformal symbology (FC); iv) Flight Guidance (FG) symbology; v) Synthetic Vision System (SVS); and vi) Radar Obstacles (RO) detection system. It has been already demonstrated that in cognitive tasks, when the cerebral workload increases the EEG power spectral density (PSD) in theta band over frontal areas increases, and the EEG PSD in alpha band decreases over parietal areas. A mental workload index (MWL) has been here defined as the ratio between the frontal theta and parietal alpha EEG PSD values. Such index has been used for testing and comparing the different avionic technologies. Results suggested that the HUD provided a significant (p<;.05) workload reduction across all the flight scenarios with respect to the other technologies. In addition, the simultaneous use of FC and FG technologies (FC+FG) produced a significant decrement of the workload (p<;.01) with respect to the use of only the FC. Moreover, the use of the SVS technology provided on Head Down Display (HDD) with the simultaneous use of FC+FG and the RO seemed to produce a lower cerebral workload when compared with the use of only the FC. Interestingly, the workload estimation by means of subjective measures, provided by pilots through a NASA-TLX questionnaire, did not provide any significant differences among the different flight scenarios. These results suggested that the proposed MWL cognitive neurometrics could be used as a reliable measure of the users mental workload, being a valid indicator for the comparison and the test of different avionic technologies.

Self-calibration algorithm in an asynchronous P300-based brain–computer interface

OBJECTIVE Reliability is a desirable characteristic of brain-computer interface (BCI) systems when they are intended to be used under non-experimental operating conditions. In addition, their overall usability is influenced by the complex and frequent procedures that are required for configuration and calibration. Earlier studies examined the issue of asynchronous control in P300-based BCIs, introducing dynamic stopping and automatic control suspension features. This report proposes and evaluates an algorithm for the automatic recalibration of the classifiers parameters using unsupervised data. APPROACH Ten healthy subjects participated in five P300-based BCI sessions throughout a single day. First, we examined whether continuous adaptation of control parameters improved the accuracy of the asynchronous system over time. Then, we assessed the performance of the self-calibration algorithm with respect to the no-recalibration and supervised calibration conditions with regard to system accuracy and communication efficiency. MAIN RESULTS Offline tests demonstrated that continuous adaptation of the control parameters significantly increased the communication efficiency of asynchronous P300-based BCIs. The self-calibration algorithm correctly assigned labels to unsupervised data with 95% accuracy, effecting communication efficiency that was comparable with that of supervised repeated calibration. SIGNIFICANCE Although additional online tests that involve end-users under non-experimental conditions are needed, these preliminary results are encouraging, from which we conclude that the self-calibration algorithm is a promising solution to improve P300-based BCI usability and reliability.

On the Use of Cognitive Neurometric Indexes in Aeronautic and Air Traffic Management Environments

In this paper the use of neurophysiological indexes for an objective evaluation of mental workload, during an ecological Air Traffic Management (ATM) task, has been proposed.