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

Scalability of Optical Interconnects Based on Microring Resonators

This letter investigates the use of optical microring resonators as switching elements (SEs) in large optical interconnection fabrics. We introduce a simple physical-layer model to assess scalability in crossbar- and Benes-based architectures.We also propose a new dilated SE that improves scalability to build fabrics of several terabits per second of aggregate capacit

Boosting the Performance of PC-based Software Routers with FPGA-enhanced Network Interface Cards

The research community is devoting increasing attention to software routers based on off-the-shelf hardware and open-source operating systems running on the personalcomputer (PC) architecture. Today's high-end PCs are equipped with peripheral component interconnect (PCI) shared buses enabling them to easily fit into the multi-gigabit-per-second routing segment, for a price much lower than that of commercial routers. However, commercially-available PC network interface cards (NICs) lack programmability, and require not only packets to cross the PCI bus twice, but also to be processed in software by the operating system, strongly reducing the achievable forwarding rate. It is therefore interesting to explore the performance of customizable NICs based on field-programmable gate array (FPGA) logic devices we developed and assess how well they can overcome the limitations of today's commercially-available NIC

WSN and RFID Integration in the IoT scenario: an Advanced Safety System for Industrial Plants

The paper proposes and discusses the integration of WSN and RFID technologies in the IoT scenario. The proposed approach is based on the REST paradigm, thanks to which the two technologies can be seamless integrated by representing sensors, actuators and RFID related data as network resources globally addressable through state-of-the-art IoT protocols. The integration approach is detailed for the Smart Factory use case by proposing and developing an advanced IoT-based WSN and RFID integrated solution aiming at improving safety in industrial plants. The developed system can guarantee a safe access to factory dangerous areas in which safety equipments are required. In the paper, the system design is first presented, then, all the developed hardware and software solutions are described before presenting system performance results in a real test bed. System performance are reported in terms of response time and accuracy for authorization control and location tracking applications

A Poll Oriented Classifier for Affective Brain Computer Interfaces

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A Virtual Glove System for the Hand Rehabilitation based on Two Orthogonal LEAP Motion Controllers

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A new case of de novo 6q24.2-q25.2 deletion on paternal chromosome 6 with growth hormone deficiency: A twelve-year follow-up and literature review

BACKGROUND: Deletions on the distal portion of the long arm of chromosome 6 are relatively uncommon, and only a small number occurs in the paternal copy, causing growth abnormalities. As a result, extensive clinical descriptions are lacking. CASE PRESENTATION: We describe a male of Italian descent born at 35 weeks by elective caesarean delivery presenting hypoplastic left colon, bilateral inguinal hernia, dysplastic tricuspid and pulmonary valves, premature ventricular contractions, recurrent otitis media, poor feeding, gastro-oesophageal reflux, bilateral pseudopapilledema, and astigmatism. He also showed particular facial dysmorphisms and postnatal growth failure. Early psychomotor development was mildly delayed. At 3.75 years, he was evaluated for severe short stature (−2.98 SD) and delayed bone age. He showed an insulin-like growth factor 1 concentration (IGF-1) in the low-normal range. Growth hormone stimulation tests showed a low response to clonidine and insulin. Magnetic resonance imaging showed hypophyseal hypoplasia. Genetic evaluation by Single Nucleotide Polymorphism arrays showed a de novo 6q24.2-q25.2 deletion on paternal chromosome 6. CONCLUSION: We confirm that this is a new congenital malformation syndrome associated with a deletion of 6q24.2-q25.2 on paternal chromosome 6. We suggest evaluating the growth hormone axis in children with 6q24.2-q25.2 deletions and growth failure

Classification of Emotional Signals from the DEAP dataset

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