A robust and self-paced BCI system based on a four class SSVEP paradigm: algorithms and protocols for a high-transfer-rate direct brain communication.

In this paper, we present, with particular focus on the adopted processing and identification chain and protocol-related solutions, a whole self-paced brain-computer interface system based on a 4-class steady-state visual evoked potentials (SSVEPs) paradigm. The proposed system incorporates an automated spatial filtering technique centred on the common spatial patterns (CSPs) method, an autoscaled and effective signal features extraction which is used for providing an unsupervised biofeedback, and a robust self-paced classifier based on the discriminant analysis theory. The adopted operating protocol is structured in a screening, training, and testing phase aimed at collecting user-specific information regarding best stimulation frequencies, optimal sources identification, and overall system processing chain calibration in only a few minutes. The system, validated on 11 healthy/pathologic subjects, has proven to be reliable in terms of achievable communication speed (up to 70 bit/min) and very robust to false positive identifications.

Pervasive technology in Neonatal Intensive Care Unit: a prototype for newborns unobtrusive monitoring.

Pervasive computing research is introducing new perspectives in a wide range of applications, including healthcare domain. In this study we explore the possibility to realize a prototype of a system for unobtrusive recording and monitoring of multiple biological parameters on premature newborns hospitalized in the Neonatal Intensive Care Unit (NICU). It consists of three different units: a sensitized belt for Electrocardiogram (ECG) and chest dilatation monitoring, augmented with extrinsic transducers for temperature and respiratory activity measure, a device for signals pre-processing, sampling and transmission through Bluetooth(R) (BT) technology to a remote PC station and a software for data capture and post-processing. Preliminary results obtained by monitoring babies just discharged from the ward demonstrated the feasibility of the unobtrusive monitoring on this kind of subjects and open a new scenario for premature newborns monitoring and developmental cares practice in NICU.

A Wearable Home BCI system: preliminary results with SSVEP protocol.

This paper presents and discusses the realization and the performances of a wearable system for EEG-based BCI applications. The system (called Kimera) consists of a two-layer hardware architecture (the wireless acquisition and transmission board based on a Bluetooth ® ARM chip, and a low power miniaturized biosignal acquisition analog front end) together with a software suite (called Bellerophonte) for the Graphic User Interface management, protocol execution, data recording, transmission and processing. The implemented BCI system was based on the SSVEP protocol, applied to a two state selection by using standards display/monitor with a couple of high efficiency LEDs. The frequency features of the signal were computed and used in the intention detection. The BCI algorithm is based on a supervised classifier implemented through a multi-class Canonical Discriminant Analysis (CDA) with a continuous realtime feedback based on the mahalanobis distance parameter. Five healthy subjects participated in the first phase for a preliminary device validation. The obtained results are very interesting and promising, being lined out to the most recent performance reported in literature with a significant improvement both in system and in classification capabilities. The user-friendliness and low cost of the Kimera& Bellerophonte platform make it suitable for the development of home BCI applications.