Giuseppe Placidi

A semi-immersive virtual reality incremental swing balance task activates prefrontal cortex: A functional near-infrared spectroscopy study

Previous functional near-infrared spectroscopy (fNIRS) studies indicated that the prefrontal cortex (PFC) is involved in the maintenance of the postural balance after external perturbations. So far, no studies have been conducted to investigate the PFC hemodynamic response to virtual reality (VR) tasks that could be adopted in the field of functional neurorehabilitation. The aim of this fNIRS study was to assess PFC oxygenation response during an incremental and a control swing balance task (ISBT and CSBT, respectively) in a semi-immersive VR environment driven by a depth-sensing camera. It was hypothesized that: i) the PFC would be bilaterally activated in response to the increase of the ISBT difficulty, as this cortical region is involved in the allocation of attentional resources to maintain postural control; and ii) the PFC activation would be greater in the right than in the left hemisphere considering its dominance for visual control of body balance. To verify these hypotheses, 16 healthy male subjects were requested to stand barefoot while watching a 3 dimensional virtual representation of themselves projected onto a screen. They were asked to maintain their equilibrium on a virtual blue swing board susceptible to external destabilizing perturbations (i.e., randomizing the forward-backward direction of the impressed pulse force) during a 3-min ISBT (performed at four levels of difficulty) or during a 3-min CSBT (performed constantly at the lowest level of difficulty of the ISBT). The center of mass (COM), at each frame, was calculated and projected on the floor. When the subjects were unable to maintain the COM over the board, this became red (error). After each error, the time required to bring back the COM on the board was calculated (returning time). An eight-channel continuous wave fNIRS system was employed for measuring oxygenation changes (oxygenated-hemoglobin, O2Hb; deoxygenated-hemoglobin, HHb) related to the PFC activation (Brodmann Areas 10, 11 and 46). The results have indicated that the errors increased between the first and the second level of difficulty of the ISBT, then decreased and remained constant; the returning time progressively increased during the first three levels of difficulty and then remained constant. During the CSBT, the errors and the returning time did not change. In the ISBT, the increase of the first three levels of difficulty was accompanied by a progressive increase in PFC O2Hb and a less consistent decrease in HHb. A tendency to plateau was observable for PFC O2Hb and HHb changes in the fourth level of difficulty of the ISBT, which could be partly explained by a learning effect. A right hemispheric lateralization was not found. A lower amplitude of increase in O2Hb and decrease in HHb was found in the PFC in response to the CSBT with respect to the ISBT. This study has demonstrated that the oxygenation increased over the PFC while performing an ISBT in a semi-immersive VR environment. These data reinforce the involvement of the PFC in attention-demanding balance tasks. Considering the adaptability of this virtual balance task to specific neurological disorders, the absence of motion sensing devices, and the motivating/safe semi-immersive VR environment, the ISBT adopted in this study could be considered valuable for diagnostic testing and for assessing the effectiveness of functional neurorehabilitation.

Design of an efficient framework for fast prototyping of customized human-computer interfaces and virtual environments for rehabilitation

Rehabilitation is often required after stroke, surgery, or degenerative diseases. It has to be specific for each patient and can be easily calibrated if assisted by human-computer interfaces and virtual reality. Recognition and tracking of different human body landmarks represent the basic features for the design of the next generation of human-computer interfaces. The most advanced systems for capturing human gestures are focused on vision-based techniques which, on the one hand, may require compromises from real-time and spatial precision and, on the other hand, ensure natural interaction experience. The integration of vision-based interfaces with thematic virtual environments encourages the development of novel applications and services regarding rehabilitation activities. The algorithmic processes involved during gesture recognition activity, as well as the characteristics of the virtual environments, can be developed with different levels of accuracy. This paper describes the architectural aspects of a framework supporting real-time vision-based gesture recognition and virtual environments for fast prototyping of customized exercises for rehabilitation purposes. The goal is to provide the therapist with a tool for fast implementation and modification of specific rehabilitation exercises for specific patients, during functional recovery. Pilot examples of designed applications and preliminary system evaluation are reported and discussed.

pH-sensitive imaging by low-frequency EPR : a model study for biological applications

The use of pH-sensitive nitroxides, in conjunction with low-frequency EPR, offers a unique opportunity for non-invasive assessment of pH values (in the range 0 to 14) in living animals. In the present study, we have investigated the potential use of pH-sensitive nitroxide free radicals in conjunction with EPR imaging techniques at low and very low frequencies (280 MHz-2.1 GHz). In particular, we have measured the hyperfine splitting (hfs) of a pH-sensitive probe at three different EPR frequencies: 280 MHz, 1.1 GHz and 2.1 GHz. We have also developed EPR imaging experiments with phantoms simulating in vivo conditions, using pH-sensitive probes at 280 MHz (spatial-spatial) and 1.1 GHz (spectral-spatial). Finally, we discuss the actual sensitivity/resolution limits of the EPR imaging techniques at low frequencies. Practical applications of this method in the biomedical field are suggested for the continuous and non-invasive localization of pH in vivo.

A real-time classification algorithm for EEG-based BCI driven by self-induced emotions

BACKGROUND AND OBJECTIVE The aim of this paper is to provide an efficient, parametric, general, and completely automatic real time classification method of electroencephalography (EEG) signals obtained from self-induced emotions. The particular characteristics of the considered low-amplitude signals (a self-induced emotion produces a signal whose amplitude is about 15% of a really experienced emotion) require exploring and adapting strategies like the Wavelet Transform, the Principal Component Analysis (PCA) and the Support Vector Machine (SVM) for signal processing, analysis and classification. Moreover, the method is thought to be used in a multi-emotions based Brain Computer Interface (BCI) and, for this reason, an ad hoc shrewdness is assumed. METHOD The peculiarity of the brain activation requires ad-hoc signal processing by wavelet decomposition, and the definition of a set of features for signal characterization in order to discriminate different self-induced emotions. The proposed method is a two stages algorithm, completely parameterized, aiming at a multi-class classification and may be considered in the framework of machine learning. The first stage, the calibration, is off-line and is devoted at the signal processing, the determination of the features and at the training of a classifier. The second stage, the real-time one, is the test on new data. The PCA theory is applied to avoid redundancy in the set of features whereas the classification of the selected features, and therefore of the signals, is obtained by the SVM. RESULTS Some experimental tests have been conducted on EEG signals proposing a binary BCI, based on the self-induced disgust produced by remembering an unpleasant odor. Since in literature it has been shown that this emotion mainly involves the right hemisphere and in particular the T8 channel, the classification procedure is tested by using just T8, though the average accuracy is calculated and reported also for the whole set of the measured channels. CONCLUSIONS The obtained classification results are encouraging with percentage of success that is, in the average for the whole set of the examined subjects, above 90%. An ongoing work is the application of the proposed procedure to map a large set of emotions with EEG and to establish the EEG headset with the minimal number of channels to allow the recognition of a significant range of emotions both in the field of affective computing and in the development of auxiliary communication tools for subjects affected by severe disabilities.

Overall design and implementation of the virtual glove

Post-stroke patients and people suffering from hand diseases often need rehabilitation therapy. The recovery of original skills, when possible, is closely related to the frequency, quality, and duration of rehabilitative therapy. Rehabilitation gloves are tools used both to facilitate rehabilitation and to control improvements by an evaluation system. Mechanical gloves have high cost, are often cumbersome, are not re-usable and, hence, not usable with the healthy hand to collect patient-specific hand mobility information to which rehabilitation should tend. The approach we propose is the virtual glove, a system that, unlike tools based on mechanical haptic interfaces, uses a set of video cameras surrounding the patient hand to collect a set of synchronized videos used to track hand movements. The hand tracking is associated with a numerical hand model that is used to calculate physical, geometrical and mechanical parameters, and to implement some boundary constraints such as joint dimensions, shape, joint angles, and so on. Besides being accurate, the proposed system is aimed to be low cost, not bulky (touch-less), easy to use, and re-usable. Previous works described the virtual glove general concepts, the hand model, and its characterization including system calibration strategy. The present paper provides the virtual glove overall design, both in real-time and in off-line modalities. In particular, the real-time modality is described and implemented and a marker-based hand tracking algorithm, including a marker positioning, coloring, labeling, detection and classification strategy, is presented for the off-line modality. Moreover, model based hand tracking experimental measurements are reported, discussed and compared with the corresponding poses of the real hand. An error estimation strategy is also presented and used for the collected measurements. System limitations and future work for system improvement are also discussed.

Post-processing noise removal algorithm for magnetic resonance imaging based on edge detection and wavelet analysis

A post-processing noise suppression technique for biomedical MRI images is presented. The described procedure recovers both sharp edges and smooth surfaces from a given noisy MRI image; it does not blur the edges and does not introduce spikes or other artefacts. The fine details of the image are also preserved. The proposed algorithm first extracts the edges from the original image and then performs noise reduction by using a wavelet de-noise method. After the application of the wavelet method, the edges are restored to the filtered image. The result is the original image with less noise, fine detail and sharp edges. Edge extraction is performed by using an algorithm based on Sobel operators. The wavelet de-noise method is based on the calculation of the correlation factor between wavelet coefficients belonging to different scales. The algorithm was tested on several MRI images and, as an example of its application, we report the results obtained from a spin echo (multi echo) MRI image of a human wrist collected with a low field experimental scanner (the signal-to-noise ratio, SNR, of the experimental image was 12). Other filtering operations have been performed after the addition of white noise on both channels of the experimental image, before the magnitude calculation. The results at SNR = 7, SNR = 5 and SNR = 3 are also reported. For SNR values between 5 and 12, the improvement in SNR was substantial and the fine details were preserved, the edges were not blurred and no spikes or other artefacts were evident, demonstrating the good performances of our method. At very low SNR (SNR = 3) our result is worse than that obtained by a simpler filtering procedure.

New experimental apparatus for multimodal resonance imaging: initial EPRI and NMRI experimental results

Electron paramagnetic resonance imaging (EPRI) is a recently developed imaging technique employed in the study of free radicals in living systems. A full understanding of many physiological and pathological processes involving free radicals has not yet been attempted. The reason for this is that whilst nuclear magnetic resonance imaging (NMRI) is able to generate very accurate images of soft tissues and organs, EPRI does not have this capability because of its sensitivity limitations and the large linewidths of paramagnetic probes. This work describes the development and optimization of a multimodal apparatus capable of performing both pulsed EPRI and NMRI experiments on the same sample. The instrument combines the possibilities offered by both techniques: the functional and biochemical information achieved with EPRI, and the high-resolution anatomical images generated by NMRI. At present, these experiments are performed by moving the sample from an EPRI spectrometer to an NMRI apparatus. Consequently, the acquisition times are very long and several problems arise in image reconstruction. On the other hand, a unique apparatus operating in the two modalities greatly reduces the acquisition times and makes it possible to relate accurately the observed distribution of electron spin density with the anatomical description of individual organs. The experiments are performed at 357 Gauss, corresponding to a resonance frequency of 1.52 MHz for NMR and 1 GHz for EPR. In the present work, a detailed description of the apparatus is reported, including the main magnet, the gradient assembly, the multimodal cavity and the transmitter and receiver systems. The preliminary experimental results obtained by this apparatus are presented.

Basis for the implementation of an EEG-based single-trial binary brain computer interface through the disgust produced by remembering unpleasant odors

In order to implement an EEG-based brain computer interface (BCI), a very large number of strategies (ranging from sensory-motor, p300, auditory based, visually based) can be used. However, no technique exists which is based on the olfactory stimulation or, better, based on the imagination of olfactory stimuli.The present paper describes an innovative paradigm, that is the voluntary brain activation with the disgust produced by remembering unpleasant odors, and a simple and robust classification method on which a single trial binary BCI can be implemented. In order to classify the signal, mainly the channels P4, C4, T8 and P8 have been used, by spanning the frequency band between 32 and 42Hz, that is a subset of the gamma band external to the bands usually occupied by other tasks (the interval between 1 and 30Hz), and the alpha band between 8 and 12Hz.Right hemisphere of the brain and gamma band of frequencies are particularly sensitive when experiencing negative emotions, such as the disgust produced by smelling or remembering unpleasant odors, while the alpha band is usually modified with concentration. This constitutes an advantage for the proposed classification technique because it is made intrinsically easy by the localization into particular positions and frequencies: different features are mostly based on different frequency bands.The choice of disgust produced by remembering unpleasant odors is twofold: smelling is an ancestral sensation which is so strong that its EEG signal is produced also in persons affected by hyposmia when they imagine an olfactory situation; it can be used without external stimulation, that is the user can decide freely when and if activate it.The proposed method and the experimental setup are described and a series of experimental measurements are presented and discussed. The accuracy of the proposed method is also evaluated and the reached levels are about 90%. The proposed system can be a useful communication alternative for disabled people that cannot use other BCI paradigms.

A low-cost real time virtual system for postural stability assessment at home

BACKGROUND AND OBJECTIVE The degeneration of the balance control system in the elderly and in many pathologies requires measuring the equilibrium conditions very often. In clinical practice, equilibrium control is commonly evaluated by using a force platform (stabilometric platform) in a clinical environment. In this paper, we demonstrate how a simple movement analysis system, based on a 3D video camera and a 3D real time model reconstruction of the human body, can be used to collect information usually recorded by a physical stabilometric platform. METHODS The algorithm used to reconstruct the human body model as a set of spheres is described and discussed. Moreover, experimental measurements and comparisons with data collected by a physical stabilometric platform are also reported. The measurements were collected on a set of 6 healthy subjects to whom a change in equilibrium condition was stimulated by performing an equilibrium task. RESULTS The experimental results showed that more than 95% of data collected by the proposed method were not significantly different from those collected by the classic platform, thus confirming the usefulness of the proposed system. CONCLUSIONS The proposed virtual balance assessment system can be implemented at low cost (about 500

Prefrontal Cortex Activation Upon a Demanding Virtual Hand-Controlled Task: A New Frontier for Neuroergonomics

) and, for this reason, can be considered a home use medical device. On the contrary, astabilometric platform has a cost of about 10,000