A multigrasp, bidirectional hand prosthesis delivers dynamic sensory feedback, allowing a user with a hand amputation to achieve fine grasping force control and realistic object sensing.

Goal: This paper reports on a novel algorithm for the analysis of electrodermal activity (EDA) using methods of convex optimization. EDA can be considered as one of the most common observation channels of sympathetic nervous system activity, and manifests itself as a change in electrical properties of the skin, such as skin conductance (SC). Methods: The proposed model describes SC as the sum of three terms: the phasic component, the tonic component, and an additive white Gaussian noise term incorporating model prediction errors as well as measurement errors and artifacts. This model is physiologically inspired and fully explains EDA through a rigorous methodology based on Bayesian statistics, mathematical convex optimization, and sparsity. Results: The algorithm was evaluated in three different experimental sessions to test its robustness to noise, its ability to separate and identify stimulus inputs, and its capability of properly describing the activity of the autonomic nervous system in response to strong affective stimulation. Significance: Results are very encouraging, showing good performance of the proposed method and suggesting promising future applicability, e.g., in the field of affective computing.

Emotion recognition through computational modeling and analysis of physiological signals has been widely investigated in the last decade. Most of the proposed emotion recognition systems require relatively long-time series of multivariate records and do not provide accurate real-time characterizations using short-time series. To overcome these limitations, we propose a novel personalized probabilistic framework able to characterize the emotional state of a subject through the analysis of heartbeat dynamics exclusively. The study includes thirty subjects presented with a set of standardized images gathered from the international affective picture system, alternating levels of arousal and valence. Due to the intrinsic nonlinearity and nonstationarity of the RR interval series, a specific point-process model was devised for instantaneous identification considering autoregressive nonlinearities up to the third-order according to the Wiener-Volterra representation, thus tracking very fast stimulus-response changes. Features from the instantaneous spectrum and bispectrum, as well as the dominant Lyapunov exponent, were extracted and considered as input features to a support vector machine for classification. Results, estimating emotions each 10 seconds, achieve an overall accuracy in recognizing four emotional states based on the circumplex model of affect of 79.29%, with 79.15% on the valence axis, and 83.55% on the arousal axis.

In this paper we propose a brain-computer interface (BCI) mouse based on P300 waves in electroencephalogram (EEG) signals. The system is analogue in that at no point a binary decision is made as to whether or not a P300 was actually produced in response to the stimuli. Instead, the 2D motion of the pointer on the screen, using a novel BCI paradigm, is controlled by directly combining the amplitudes of the output produced by a filter in the presence of different stimuli. This filter and the features to be combined within it are optimised by an evolutionary algorithm.

Reliable and effective noninvasive measures of sympathetic and parasympathetic peripheral outflow are of crucial importance in cardiovascular physiology. Although many techniques have been proposed to take up this long-lasting challenge, none has proposed a satisfying discrimination of the dynamics of the two separate branches. Spectral analysis of heart rate variability is the most currently used technique for such assessment. Despite its widespread use, it has been demonstrated that the subdivision in the low-frequency (LF) and high-frequency (HF) bands does not fully reflect separate influences of the sympathetic and parasympathetic branches, respectively, mainly due to their simultaneous action in the LF. Two novel heartbeat-derived autonomic measures, the sympathetic activity index (SAI) and parasympathetic activity index (PAI), are proposed to separately assess the time-varying autonomic nervous system synergic functions. Their efficacy is validated in landmark autonomic maneuvers generally employed in clinical settings. The novel measures move beyond the classical frequency domain paradigm through identification of a set of coefficients associated with a proper combination of Laguerre base functions. The resulting measures were compared with the traditional LF and HF power. A total of 236 ECG recordings were analyzed for validation, including autonomic outflow changes elicited by procedures of different nature and temporal variation, such as postural changes, lower body negative pressure, and handgrip tests. The proposed SAI-PAI measures consistently outperform traditional frequency-domain indexes in tracking expected instantaneous autonomic variations, both vagal and sympathetic, and may aid clinical decision making, showing reduced intersubject variability and physiologically plausible dynamics. NEW & NOTEWORTHY While it is possible to obtain reliable estimates of parasympathetic activity from the ECG, a satisfying method to disentangle the sympathetic component from HRV has not been proposed yet. To overcome this long-lasting limitation, we propose two novel HRV-based indexes, the sympathetic and parasympathetic activity indexes.