Francesco Leotta

A Brain-Computer Interface as Input Channel for a Standard Assistive Technology Software

Recently brain-computer interface (BCI) control was integrated into the commercial assistive technology product QualiWORLD (QualiLife Inc., Paradiso-Lugano, CH). Usability of the first prototype was evaluated in terms of effectiveness (accuracy), efficiency (information transfer rate and subjective workload/NASA Task Load Index) and user satisfaction (Quebec User Evaluation of Satisfaction with assistive Technology, QUEST 2.0) by four end-users with severe disabilities. Three assistive technology experts evaluated the device from a third person perspective. The results revealed high performance levels in communication and internet tasks. Users and assistive technology experts were quite satisfied with the device. However, none could imagine using the device in daily life without improvements. Main obstacles were the EEG-cap and low speed.

Tools for Brain-Computer Interaction: A General Concept for a Hybrid BCI

The aim of this work is to present the development of a hybrid Brain-Computer Interface (hBCI) which combines existing input devices with a BCI. Thereby, the BCI should be available if the user wishes to extend the types of inputs available to an assistive technology system, but the user can also choose not to use the BCI at all; the BCI is active in the background. The hBCI might decide on the one hand which input channel(s) offer the most reliable signal(s) and switch between input channels to improve information transfer rate, usability, or other factors, or on the other hand fuse various input channels. One major goal therefore is to bring the BCI technology to a level where it can be used in a maximum number of scenarios in a simple way. To achieve this, it is of great importance that the hBCI is able to operate reliably for long periods, recognizing and adapting to changes as it does so. This goal is only possible if many different subsystems in the hBCI can work together. Since one research institute alone cannot provide such different functionality, collaboration between institutes is necessary. To allow for such a collaboration, a new concept and common software framework is introduced. It consists of four interfaces connecting the classical BCI modules: signal acquisition, preprocessing, feature extraction, classification, and the application. But it provides also the concept of fusion and shared control. In a proof of concept, the functionality of the proposed system was demonstrated.

Out of the frying pan into the fire--the P300-based BCI faces real-world challenges.

Brain-computer interfaces (BCIs) have been investigated for more than 20 years. Many BCIs use noninvasive electroencephalography as a measurement technique and the P300 event-related potential as an input signal (P300 BCI). Since the first experiment with a P300 BCI system in 1988 by Farwell and Donchin, not only data processing has improved but also stimuli presentation has been varied and a plethora of applications was developed and refined. Nowadays, these applications are facing the challenge of being transferred from the research laboratory into real-life situations to serve motor-impaired people in their homes as assistive technology.

Workload measurement in a communication application operated through a P300-based brain-computer interface

Advancing the brain-computer interface (BCI) towards practical applications in technology-based assistive solutions for people with disabilities requires coping with problems of accessibility and usability to increase user acceptance and satisfaction. The main objective of this study was to introduce a usability-oriented approach in the assessment of BCI technology development by focusing on evaluation of the users subjective workload and satisfaction. The secondary aim was to compare two applications for a P300-based BCI. Eight healthy subjects were asked to use an assistive technology solution which integrates the P300-based BCI with commercially available software under two conditions--visual stimuli needed to evoke the P300 response were either overlaid onto the applications graphical user interface or presented on a separate screen. The two conditions were compared for effectiveness (level of performance), efficiency (subjective workload measured by means of NASA-TXL) and satisfaction of the user. Although no significant difference in usability could be detected between the two conditions, the methodology proved to be an effective tool to highlight weaknesses in the technical solution.

EEG-based Brain-Computer Interface to support post-stroke motor rehabilitation of the upper limb

Brain-Computer Interfaces (BCIs) process brain activity in real time, and mediate non-muscular interaction between and individual and the environment. The subserving algorithms can be used to provide a quantitative measurement of physiological or pathological cognitive processes - such as Motor Imagery (MI) - and feed it back the user. In this paper we propose the clinical application of a BCI-based rehabilitation device, to promote motor recovery after stroke. The BCI-based device and the therapy exploiting its use follow the same principles that drive classical neuromotor rehabilitation, and (i) provides the physical therapist with a monitoring instrument, to assess the patients participation in the rehabilitative cognitive exercise; (ii) assists the patient in the practice of MI. The device was installed in the ward of a rehabilitation hospital and a group of 29 patients were involved in its testing. Among them, eight have already undergone a one month training with the device, as an add-on to the regular therapy. An improved system, which includes analysis of Electromyographic (EMG) patterns and Functional Electrical Stimulation (FES) of the arm muscles, is also under clinical evaluation. We found that the rehabilitation exercise based on BCI mediated neurofeedback mechanisms enables a better engagement of motor areas with respect to motor imagery alone and thus it can promote neuroplasticity in brain regions affected by a cerebrovascular accident. Preliminary results also suggest that the functional outcome of motor rehabilitation may be improved by the use of the proposed device.

Applying Process Mining to Smart Spaces: Perspectives and Research Challenges

A software system managing a smart space takes, among its inputs, models of human behavior; such models are usually difficult to obtain and to validate. The employment of techniques from business process modeling and mining may represent a solution to both the problems, but a set of challenges need to be faced in order to cope with major differences between human activities and business processes. In this work we provide insights about these challenges, and propose further research activities to tackle them.

Synthesizing daily life logs through gaming and simulation

In the recent years there has been a growing interest in the design and implementation of smart homes, and smart buildings in general. The evaluation of approaches in this area typically requires massive datasets of measurements from deployed sensors in real prototypes. While a few datasets obtained by real smart homes are freely available, they are not sufficient for comparing different approaches and techniques in a variety of configurations. In this work, we propose a smart home dataset generation strategy based on a simulated environment populated with virtual autonomous agents, sensors and devices which allow to customize and reproduce a smart space using a series of useful parameters. The simulation is based on declarative process models for modeling habits performed by agents, an action theory for realizing low-level atomic actions, and a 3D virtual execution environment. We show how different configurations generate a variety of sensory logs that can be used as input to a state-of-the-art activity recognition technique in order to evaluate its performance under parametrized scenarios, as well as provide guidelines for actually building real smart homes.

PLaTHEA: a marker-less people localization and tracking system for home automation

Current smart spaces require more and more sophisticated sensors able to acquire the state of the environment in order to provide advanced and customized services. Among the most important environmental variables, locations of users and their identities represent a primary concern for smart home applications. Despite some years of investigation in indoor positioning, the availability of systems designed as components pluggable into complex home automation platforms is limited. We present People Localization and Tracking for HomE Automation (PLaTHEA), a vision‐based indoor localization system specifically tailored for Ambient Assisted Living applications. PLaTHEA features a novel technique to acquire a stereo video stream from a couple of independent (and not synchronized) network‐attached cameras, thus easing its physical deployment. The input stream is processed by integrating well‐known techniques with a novel tracking approach targeted to indoor spaces. The system has a modular architecture that offers clear interfaces exposed as Web services, and it can run on off‐the‐shelf and cheap hardware (both in terms of sensing devices and computing units). We evaluated PLaTHEA in real usage conditions and reported the measured performance in terms of precision and accuracy. Low light, crowded and large monitored environments might slightly decrease the performance of the system; nevertheless, the results here presented show that it is perfectly suitable to be employed in the typical domestic day‐to‐day life settings. Copyright

Toward domotic appliances control through a self-paced P300-based BCI

During recent years there has been a growing interest in Brain Computer Interface (BCI) systems as an alternative means of interaction with the external world for people with severe motor disabilities. The use of the P300 event-related potentials as control feature allows users to choose between various options (letters or icons) requiring a very short calibration phase. The aim of this work is to improve performances and flexibility of P300 based BCIs. An efficient BCI system should be able to understand users intentions from the ongoing EEG, abstaining from doing a selection when the user is engaged in a different activity, and changing its speed of selection depending on current users attention level. Our self-paced system addresses all these issues representing an important step beyond the classical synchronous P300 BCI that forces the user in a continuous control task. Experimentation has been performed on 10 healthy volunteers acting on a BCI-controlled domestic environment in order to demonstrate the potential usability of BCI systems in everyday life. Results show that the self-paced BCI increases information transfer rate with respect to the synchronous one, being very robust, at the same time, in avoiding false negatives when the user is not engaged in a control task.

Itemset-based mining of constraints for enacting smart environments

In order to automatically control the environment, smart systems should have sufficient rules, which describe expected systems behavior. While such rules may be added man-ually, usually this requires considerable efforts, often surpassing those that users are willing to spend to setup the system. In this paper, we propose a novel technique to mine such rules automatically, given a sensor log from the environment. In particular, we mine itemsets, but we consider abnormal drops in the frequency of variable state combinations w.r.t. the frequency of their subsets, which represent undesirability of these combinations. We evaluate the technique both on simulated and real datasets, showing that the approach is effective and promising for further extensions.