Andrea Petracca

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.

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

A novel semi-immersive virtual reality visuo-motor task activates ventrolateral prefrontal cortex: a functional near-infrared spectroscopy study.

and requires periodical calibration. The proposed system does not require periodical calibration, as is necessary for stabilometric force platforms, and it is easy to use. In future, the proposed system with little integration can be used, besides being an emulator of a stabilometric platform, also to recognize and track, in real time, head, legs, arms and trunk, that is to collect information actually obtained by sophisticated optoelectronic systems.

A Modular Framework for EEG Web Based Binary Brain Computer Interfaces to Recover Communication Abilities in Impaired People

Functional near-infrared spectroscopy (fNIRS) is a non-invasive vascular-based functional neuroimaging technology that can assess, simultaneously from multiple cortical areas, concentration changes in oxygenated-deoxygenated hemoglobin at the level of the cortical microcirculation blood vessels. fNIRS, with its high degree of ecological validity and its very limited requirement of physical constraints to subjects, could represent a valid tool for monitoring cortical responses in the research field of neuroergonomics. In virtual reality (VR) real situations can be replicated with greater control than those obtainable in the real world. Therefore, VR is the ideal setting where studies about neuroergonomics applications can be performed. The aim of the present study was to investigate, by a 20-channel fNIRS system, the dorsolateral/ventrolateral prefrontal cortex (DLPFC/VLPFC) in subjects while performing a demanding VR hand-controlled task (HCT). Considering the complexity of the HCT, its execution should require the attentional resources allocation and the integration of different executive functions. The HCT simulates the interaction with a real, remotely-driven, system operating in a critical environment. The hand movements were captured by a high spatial and temporal resolution 3-dimensional (3D) hand-sensing device, the LEAP motion controller, a gesture-based control interface that could be used in VR for tele-operated applications. Fifteen University students were asked to guide, with their right hand/forearm, a virtual ball (VB) over a virtual route (VROU) reproducing a 42 m narrow road including some critical points. The subjects tried to travel as long as possible without making VB fall. The distance traveled by the guided VB was 70.2 ± 37.2 m. The less skilled subjects failed several times in guiding the VB over the VROU. Nevertheless, a bilateral VLPFC activation, in response to the HCT execution, was observed in all the subjects. No correlation was found between the distance traveled by the guided VB and the corresponding cortical activation. These results confirm the suitability of fNIRS technology to objectively evaluate cortical hemodynamic changes occurring in VR environments. Future studies could give a contribution to a better understanding of the cognitive mechanisms underlying human performance either in expert or non-expert operators during the simulation of different demanding/fatiguing activities.

EEG-detected olfactory imagery to reveal covert consciousness in minimally conscious state

OBJECTIVE In the last few years, the interest in applying virtual reality systems for neurorehabilitation is increasing. Their compatibility with neuroimaging techniques, such as functional near-infrared spectroscopy (fNIRS), allows for the investigation of brain reorganization with multimodal stimulation and real-time control of the changes occurring in brain activity. The present study was aimed at testing a novel semi-immersive visuo-motor task (VMT), which has the features of being adopted in the field of neurorehabilitation of the upper limb motor function. APPROACH A virtual environment was simulated through a three-dimensional hand-sensing device (the LEAP Motion Controller), and the concomitant VMT-related prefrontal cortex (PFC) response was monitored non-invasively by fNIRS. Upon the VMT, performed at three different levels of difficulty, it was hypothesized that the PFC would be activated with an expected greater level of activation in the ventrolateral PFC (VLPFC), given its involvement in the motor action planning and in the allocation of the attentional resources to generate goals from current contexts. Twenty-one subjects were asked to move their right hand/forearm with the purpose of guiding a virtual sphere over a virtual path. A twenty-channel fNIRS system was employed for measuring changes in PFC oxygenated-deoxygenated hemoglobin (O2Hb/HHb, respectively). MAIN RESULTS A VLPFC O2Hb increase and a concomitant HHb decrease were observed during the VMT performance, without any difference in relation to the task difficulty. SIGNIFICANCE The present study has revealed a particular involvement of the VLPFC in the execution of the novel proposed semi-immersive VMT adoptable in the neurorehabilitation field.

A Classification Algorithm for Electroencephalography Signals by Self-Induced Emotional Stimuli

A Brain Computer Interface (BCI) allows communication for impaired people unable to express their intention with common channels. Electroencephalography (EEG) represents an effective tool to allow the implementation of a BCI. The present paper describes a modular framework for the implementation of the graphic interface for binary BCIs based on the selection of symbols in a table. The proposed system is also designed to reduce the time required for writing text. This is made by including a motivational tool, necessary to improve the quality of the collected signals, and by containing a predictive module based on the frequency of occurrence of letters in a language, and of words in a dictionary. The proposed framework is described in a top-down approach through its modules: signal acquisition, analysis, classification, communication, visualization, and predictive engine. The framework, being modular, can be easily modified to personalize the graphic interface to the needs of the subject who has to use the BCI and it can be integrated with different classification strategies, communication paradigms, and dictionaries/languages. The implementation of a scenario and some experimental results on healthy subjects are also reported and discussed: the modules of the proposed scenario can be used as a starting point for further developments, and application on severely disabled people under the guide of specialized personnel.

A virtual ball task driven by forearm movements for neuro-rehabilitation

Abstract Primary objective: To reveal covert abilities in a minimally conscious state (MCS) through an innovative activation paradigm based on olfactory imagery. Research design: Case study. Methods and procedures: A patient in MCS was asked to ‘imagine an unpleasant odour’ or to ‘relax’ in response to the appearance on a screen of a downward pointing arrow or a cross, respectively. Electrophysiological responses to stimuli were investigated by means of an 8-channel EEG equipment and analysed using a specific threshold algorithm. The protocol was repeated for 10 sessions separated from each other by 2 weeks. Accuracy, defined as the number of successes with respect to the total number of trials, was used to evaluate the number of times in which the classification strategy was successful. Main outcomes and results: Analyses of accuracy showed that the patient was able to activate and to relax himself purposefully and that he optimized his performances with the number of sessions, probably as a result of training-related improvements. Conclusions: Subtle signs of consciousness may be under-estimated and need to be revealed through specific activation tasks. This paradigm may be useful to detect covert signs of consciousness, especially when patients are precluded from carrying out more complex cognitive tasks.

Classification strategies for a single-trial binary Brain Computer Interface based on remembering unpleasant odors

The aim of this paper is to propose a real-time classification algorithm for the low-amplitude electroencephalography (EEG) signals, such as those produced by remembering an unpleasant odor, to drive a brain-computer interface. The peculiarity of these EEG signals is that they require ad hoc signals preprocessing by wavelet decomposition, and the definition of a set of features able to characterize the signals and to discriminate among different conditions. The proposed method is completely parameterized, aiming at a multiclass classification and it might be considered in the framework of machine learning. It is a two stages algorithm. The first stage is offline and it is devoted to the determination of a suitable set of features and to the training of a classifier. The second stage, the real-time one, is to test the proposed method on new data. In order to avoid redundancy in the set of features, the principal components analysis is adapted to the specific EEG signal characteristics and it is applied; the classification is performed through the support vector machine. Experimental tests on ten subjects, demonstrating the good performance of the algorithm in terms of both accuracy and efficiency, are also reported and discussed.