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.

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.

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This paper discusses the development of a four command BCI system. This system is composed of a wearable electroencephalogram acquisition unit interfaced to a computer by a wireless Bluetooth (BT) connection. The implemented system relies on the steady-state visual evoked potential (SSVEP) protocol applied to a four selection system. In order to achieve the maximum reliability against false positives a five class classifier was used considering the idle state as an independent class. In order to maximize the usability of the system a two channel solution was tested and adopted. The BCI algorithm was based on a supervised multi-class classifier implemented by combining different binary regularized linear discriminant analysis (RLDA) classifiers. The biofeedback was evaluated by combining the resultant time signed distance with quality index related to the number of coherent identification obtained with the one-vs-all approach.

In this study we explored the possibility to realize a low power device for Cardiac Output continuous monitoring based on impedance cardiography technique. We assessed the possibility to develop a system able to record data allow an intra-subjective analysis based on the daily variations of this measure. The device was able to acquire and to send signals using a wireless Bluetooth transmission. The electronic circuit was designed in order to minimize power consumption, dimension and weight. The reported results were interesting for what concerns the power consumption and then noise level. In this way was obtained a wearable device that will permit to define specific clinical protocols based on continuous monitoring of the Cardiac Output signal.