Francesca Schettini

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.

Attention and P300-based BCI performance in people with amyotrophic lateral sclerosis

The purpose of this study was to investigate the support of attentional and memory processes in controlling a P300-based brain-computer interface (BCI) in people with amyotrophic lateral sclerosis (ALS). Eight people with ALS performed two behavioral tasks: (i) a rapid serial visual presentation (RSVP) task, screening the temporal filtering capacity and the speed of the update of the attentive filter, and (ii) a change detection task, screening the memory capacity and the spatial filtering capacity. The participants were also asked to perform a P300-based BCI spelling task. By using correlation and regression analyses, we found that only the temporal filtering capacity in the RSVP task was a predictor of both the P300-based BCI accuracy and of the amplitude of the P300 elicited performing the BCI task. We concluded that the ability to keep the attentional filter active during the selection of a target influences performance in BCI control.

Accuracy of a P300 Speller for People with Motor Impairments: a Comparison

A Brain-Computer Interface (BCI) provides a completely new output pathway that can provide an additional option for a person to express himself/her self if he/she suffers a disorder like amyotrophic lateral sclerosis (ALS), brainstem stroke, brain or spinal cord injury or other diseases which impair the function of the common output pathways which are responsible for the control of muscles. For a P300 based BCI a matrix of randomly flashing characters is presented to the participant. To spell a character the person has to attend to it and to count how many times the character flashes. Although most BCIs are designed to help people with disabilities, they are mainly tested on healthy, young subjects who may achieve better results than people with impairments. In this study we compare measurements, performed on people suffering motor impairments, such as stroke or ALS, to measurements performed on healthy people. The overall accuracy of the persons with motor impairments reached 70.1% in comparison to 91% obtained for the group of healthy subjects. When looking at single subjects, one interesting example shows that under certain circumstances, when it is difficult for a patient to concentrate on one character for a longer period of time, the accuracy is higher when fewer flashes (i.e., stimuli) are presented. Furthermore, the influence of several tuning parameters is discussed as it shows that for some participants adaptations for achieving valuable spelling results are required. Finally, exclusion criteria for people who are not able to use the device are defined.

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).

Assistive Device With Conventional, Alternative, and Brain-Computer Interface Inputs to Enhance Interaction With the Environment for People With Amyotrophic Lateral Sclerosis: A Feasibility and Usability Study

OBJECTIVE To evaluate the feasibility and usability of an assistive technology (AT) prototype designed to be operated with conventional/alternative input channels and a P300-based brain-computer interface (BCI) in order to provide users who have different degrees of muscular impairment resulting from amyotrophic lateral sclerosis (ALS) with communication and environmental control applications. DESIGN Proof-of-principle study with a convenience sample. SETTING An apartment-like space designed to be fully accessible by people with motor disabilities for occupational therapy, placed in a neurologic rehabilitation hospital. PARTICIPANTS End-users with ALS (N=8; 5 men, 3 women; mean age ± SD, 60 ± 12 y) recruited by a clinical team from an ALS center. INTERVENTIONS Three experimental conditions based on (1) a widely validated P300-based BCI alone; (2) the AT prototype operated by a conventional/alternative input device tailored to the specific end-users residual motor abilities; and (3) the AT prototype accessed by a P300-based BCI. These 3 conditions were presented to all participants in 3 different sessions. MAIN OUTCOME MEASURES System usability was evaluated in terms of effectiveness (accuracy), efficiency (written symbol rate, time for correct selection, workload), and end-user satisfaction (overall satisfaction) domains. A comparison of the data collected in the 3 conditions was performed. RESULTS Effectiveness and end-user satisfaction did not significantly differ among the 3 experimental conditions. Condition III was less efficient than condition II as expressed by the longer time for correct selection. CONCLUSIONS A BCI can be used as an input channel to access an AT by persons with ALS, with no significant reduction of usability.

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.

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.

Developing brain-computer interfaces from a user-centered perspective: Assessing the needs of persons with amyotrophic lateral sclerosis, caregivers, and professionals

By focus group methodology, we examined the opinions and requirements of persons with ALS, their caregivers, and health care assistants with regard to developing a brain-computer interface (BCI) system that fulfills the users needs. Four overarching topics emerged from this analysis: 1) lack of information on BCI and its everyday applications; 2) importance of a customizable system that supports individuals throughout the various stages of the disease; 3) relationship between affectivity and technology use; and 4) importance of individuals retaining a sense of agency. These findings should be considered when developing new assistive technology. Moreover, the BCI community should acknowledge the need to bridge experimental results and its everyday application.

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.