Kurtosis-based detection of intracranial high-frequency oscillations for the identification of the seizure onset zone

Pathological High-Frequency Oscillations (HFOs) have been recently proposed as potential biomarker of the seizure onset zone (SOZ) and have shown superior accuracy to interictal epileptiform discharges in delineating its anatomical boundaries. Characterization of HFOs is still in its infancy and this is reflected in the heterogeneity of analysis and reporting methods across studies and in clinical practice. The clinical approach to HFOs identification and quantification usually still relies on visual inspection of EEG data. In this study, we developed a pipeline for the detection and analysis of HFOs. This includes preliminary selection of the most informative channels exploiting statistical properties of the pre-ictal and ictal intracranial EEG (iEEG) time series based on spectral kurtosis, followed by wavelet-based characterization of the time-frequency properties of the signal. We performed a preliminary validation analyzing EEG data in the ripple frequency band (80-250[Formula: see text]Hz) from six patients with drug-resistant epilepsy who underwent pre-surgical evaluation with stereo-EEG (SEEG) followed by surgical resection of pathologic brain areas, who had at least two-year positive post-surgical outcome. In this series, kurtosis-driven selection and wavelet-based detection of HFOs had average sensitivity of 81.94% and average specificity of 96.03% in identifying the HFO area which overlapped with the SOZ as defined by clinical presurgical workup. Furthermore, the kurtosis-based channel selection resulted in an average reduction in computational time of 66.60%

Sensory-Glove-Based Open Surgery Skill Evaluation

Manual dexterity is one of the most important surgical skills, and yet there are limited instruments to evaluate this ability objectively. In this paper, we propose a system designed to track surgeons’ hand movements during simulated open surgery tasks and to evaluate their manual expertise. Eighteen participants, grouped according to their surgical experience, performed repetitions of two basic surgical tasks, namely single interrupted suture and simple running suture. Subjects’ hand movements were measured with a sensory glove equipped with flex and inertial sensors, tracking flexion/extension of hand joints, and wrist movement. The participants’ level of experience was evaluated discriminating manual performances using linear discriminant analysis, support vector machines, and artificial neural network classifiers. Artificial neural networks showed the best performance, with a median error rate of 0.61% on the classification of single interrupted sutures and of 0.57% on simple running sutures. Strategies to reduce sensory glove complexity and increase its comfort did not affect system performances substantially

A Fuzzy Integral Ensemble Method in Visual P300 Brain-Computer Interface

We evaluate the possibility of application of combination of classifiers using fuzzy measures and integrals to Brain-Computer Interface (BCI) based on electroencephalography. In particular, we present an ensemble method that can be applied to a variety of systems and evaluate it in the context of a visual P300-based BCI. Offline analysis of data relative to 5 subjects lets us argue that the proposed classification strategy is suitable for BCI. Indeed, the achieved performance is significantly greater than the average of the base classifiers and, broadly speaking, similar to that of the best one. Thus the proposed methodology allows realizing systems that can be used by different subjects without the need for a preliminary configuration phase in which the best classifier for each user has to be identified. Moreover, the ensemble is often capable of detecting uncertain situations and turning them from misclassifications into abstentions, thereby improving the level of safety in BCI for environmental or device control

A Fuzzy Integral Ensemble Method in Visual P300 Brain-Computer Interface

We evaluate the possibility of application of combination of classifiers using fuzzy measures and integrals to Brain-Computer Interface (BCI) based on electroencephalography. In particular, we present an ensemble method that can be applied to a variety of systems and evaluate it in the context of a visual P300-based BCI. Offline analysis of data relative to 5 subjects lets us argue that the proposed classification strategy is suitable for BCI. Indeed, the achieved performance is significantly greater than the average of the base classifiers and, broadly speaking, similar to that of the best one. Thus the proposed methodology allows realizing systems that can be used by different subjects without the need for a preliminary configuration phase in which the best classifier for each user has to be identified. Moreover, the ensemble is often capable of detecting uncertain situations and turning them from misclassifications into abstentions, thereby improving the level of safety in BCI for environmental or device control

Interacting with the environment through non-invasive brain-computer interfaces

The brain computer interface (BCI) technology allows a direct connection between brain and computer without any muscular activity required, and thus it offers a unique opportunity to enhance and/or to restore communication and actions into external word in people with severe motor disability. Here, we present the framework of the current research progresses regarding non-invasive EEG-based BCI applications specifically devoted to interact with the environment. Despite of the technological advancement, the operability of a BCI device in an out-laboratory setting (i.e. real-life condition) still remains far from being settled. The BCI control is indeed, characterized by unusual properties, when compared to more traditional inputs (long delays, noise with varying structure, long-term drifts, event-related noise, and stress effects). Current approaches to this are constituted by post hoc processing the BCI signal in order to better conform to traditional control. A long-term approach is to devise novel interaction modalities. In this regard, BCI can offer an unusual and compelling testing ground for new interaction ideas in the Human Computer Interaction field. © 2009 Springer Berlin Heidelberg

Evaluating the influence of subject-related variables on EMG-based hand gesture classification

In this study we evaluated the effect of subjectrelated variables, i.e. hand dominance, gender and experience in using, on the performances of an EMG-based system for virtual upper limb and prosthesis control. The proposed system consists in a low density EMG sensors arrangement, a purpose-built signal-conditioning electronic circuitry and a software able to classify the gestures and to replicate them via avatars. The classification algorithm was optimized in terms of feature extraction and dimensionality reduction. In its optimal configuration, the system allows to accurately discriminate five different hand gestures (accuracy = 88.85 ± 7.19%). Statistical analysis demonstrated no significant difference in classification accuracy related to hand-dominance (handedness) and to gender. In addition, maximum accuracy in dominant hand is achieved since first use of the system, whilst accuracy in classifying gestures of the non-dominant hand significantly increases with experience. These results indicate that this system can be potentially used by every trans-radial upper-limb amputee for virtual/real limb control

On ERPs detection in disorders of consciousness rehabilitation

Disorders of Consciousness (DOC) like Vegetative State (VS), and Minimally Conscious State (MCS) are clinical conditions characterized by the absence or intermittent behavioral responsiveness. A neurophysiological monitoring of parameters like Event-Related Potentials (ERPs) could be a first step to follow-up the clinical evolution of these patients during their rehabilitation phase. Eleven patients diagnosed as VS (n = 8) and MCS (n = 3) by means of the JFK Coma Recovery Scale Revised (CRS-R) underwent scalp EEG recordings during the delivery of a 3-stimuli auditory oddball paradigm, which included standard, deviant tones and the subject own name (SON) presented as a novel stimulus, administered under passive and active conditions. Four patients who showed a change in their clinical status as detected by means of the CRS-R (i.e., moved from VS to MCS), were subjected to a second EEG recording session. All patients, but one (anoxic etiology), showed ERP components such as mismatch negativity (MMN) and novelty P300 (nP3) under passive condition. When patients were asked to count the novel stimuli (active condition), the nP3 component displayed a significant increase in amplitude (p = 0.009) and a wider topographical distribution with respect to the passive listening, only in MCS. In 2 out of the 4 patients who underwent a second recording session consistently with their transition from VS to MCS, the nP3 component elicited by passive listening of SON stimuli revealed a significant amplitude increment (p < 0.05). Most relevant, the amplitude of the nP3 component in the active condition, acquired in each patient and in all recording sessions, displayed a significant positive correlation with the total scores (p = 0.004) and with the auditory sub-scores (p < 0.00001) of the CRS-R administered before each EEG recording. As such, the present findings corroborate the value of ERPs monitoring in DOC patients to investigate residual unconscious and conscious cognitive function

Standard Model, File Formats and Methods in Brain-Computer Interface Research: Why?

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