Steady-State Visual Evoked Potential (SSVEP) recognition methods use a subject’s calibration data to differentiate between brain responses, hence, providing the SSVEP-based brain–computer interfaces ...(BCIs) with high performance. However, they require sufficient calibration EEG trials to achieve that. This study develops a new method to learn from limited calibration EEG trials, and it proposes and evaluates a novel adaptive data-driven spatial filtering approach for enhancing SSVEP detection. The spatial filter learned from each stimulus utilizes temporal information from the corresponding EEG trials. To introduce the temporal information into the overall procedure, a multitask learning approach, based on the Bayesian framework, is adopted. The performance of the proposed method was evaluated into two publicly available benchmark datasets, and the results demonstrated that our method outperformed competing methods by a significant margin.
Brain biometrics have received increasing attention from the scientific community due to their unique properties compared to traditional biometric methods. Many studies have shown that EEG features ...are distinct across individuals. In this study, we propose a novel approach by considering spatial patterns of the brain's responses due to visual stimulation at specific frequencies. More specifically, we propose, for the identification of the individuals, to combine common spatial patterns with specialized deep-learning neural networks. The adoption of common spatial patterns gives us the ability to design personalized spatial filters. In addition, with the help of deep neural networks, the spatial patterns are mapped into new (deep) representations where the discrimination between individuals is performed with a high correct recognition rate. We conducted a comprehensive comparison between the performance of the proposed method and several classical methods on two steady-state visual evoked potential datasets consisting of thirty-five and eleven subjects, respectively. Furthermore, our analysis includes a large number of flickering frequencies in the steady-state visual evoked potential experiment. Experiments on these two steady-state visual evoked potential datasets showed the usefulness of our approach in terms of person identification and usability. The proposed method achieved an averaged correct recognition rate of 99% over a large number of frequencies for the visual stimulus.
Anecdotal references, preclinical, and non-randomized studies support the therapeutic potential of cannabinoids for movement disorders (MD). To create an evidenced-based point of view for patients ...and physicians, we performed a systematic review of randomized controlled trials (RCT) on the use of cannabinoids in MD. The seven RCTs found on PD used different cannabis formulations. No improvement of motor symptoms was shown in any of the two RCTs with this as primary outcome (PO), but in the nabilone group, an improvement in quality of life was documented. Of the three RCTs having levodopa-induced dyskinesia as PO, only one using nabilone showed a reduction. Anxiety and anxiety-induced tremor could be reduced in the cannabidiol group as well as anxiety and sleeping problems in the nabilone group in another RCT. In two RCTs with Tourette syndrome, an improvement in tics was revealed. From three RCTs on Huntington’s disease only one found symptoms relief using nabilone. No reduction of dystonia could be shown in the two included RCTs. The limited number of available but small and inhomogeneous RCTs precludes reliable conclusions. Therefore, more and smartly designed RCTs are urgently needed.
This paper presents a methodology for optimizing investment in data center battery storage capacity. Utility grid managers spend significant resources toward predicting and matching available power ...generation capacity to demand in real time. It is therefore essential for the success of the power industry that economic dispatch, energy efficiency, and grid security be maintained as power requirements change. This is especially challenging for microgrids during periods of peak demand due to limited available capacity. Data centers possess a unique requirement for short-term battery power supply where cost savings, emissions reduction, and reliability enhancement can be achieved through investment in additional battery capacity. To maximize these benefits, an optimization methodology is presented through a case study for an existing data center and microgrid. Here, we discuss a case study demonstrating the effectiveness of the proposed approach. For the selected mid-size data center, our results indicate monetize monthly savings of up to 10 000 and 0.5% reduction in loss of load probability while simultaneously reducing carbon footprints. The results of this work are directed toward large data centers at university and corporate campuses, microgrids, and military installations.
In this work, we propose a novel framework to recognize the cognitive and affective processes of the brain during neuromarketing-based stimuli using EEG signals. The most crucial component of our ...approach is the proposed classification algorithm that is based on a sparse representation classification scheme. The basic assumption of our approach is that EEG features from a cognitive or affective process lie on a linear subspace. Hence, a test brain signal can be represented as a linear (or weighted) combination of brain signals from all classes in the training set. The class membership of the brain signals is determined by adopting the Sparse Bayesian Framework with graph-based priors over the weights of linear combination. Furthermore, the classification rule is constructed by using the residuals of linear combination. The experiments on a publicly available neuromarketing EEG dataset demonstrate the usefulness of our approach. For the two classification tasks offered by the employed dataset, namely affective state recognition and cognitive state recognition, the proposed classification scheme manages to achieve a higher classification accuracy compared to the baseline and state-of-the art methods (more than 8% improvement in classification accuracy).
A complete Mach-Zehnder interferometer monolithically integrated on silicon is presented and employed as a refractive index and bio-chemical sensor. The device consists of broad-band light sources ...optically coupled to photodetectors through monomodal waveguides forming arrays of Mach-Zehnder interferometers, all components being monolithically integrated on silicon through mainstream silicon technology. The interferometer is photonically engineered in a way that the phase difference of light travelling through the sensing and reference arms is approximately wavelength independent. Consequently, upon effective medium changes, it becomes feasible even with a broad-band source to induce sinusoidal-type of detector photocurrents similar to the classical monochromatic counterparts. The device is completed with its fluidic and interconnect components so that on chip interferometric measurements can be performed. Examples of refractive index and protein sensing are presented to establish the potential of the proposed device for real-time in situ monitoring applications. This is the only silicon device that has achieved complete on-chip interferometry.
Meditation imparts relaxation and constitutes an important non-pharmacological intervention for people with cognitive impairment. Moreover, EEG has been widely used as a tool for detecting brain ...changes even at the early stages of Alzheimer's Disease (AD). The current study investigates the effect of meditation practices on the human brain across the AD spectrum by using a novel portable EEG headband in a smart-home environment.
Forty (40) people (13 Healthy Controls-HC, 14 with Subjective Cognitive Decline-SCD and 13 with Mild Cognitive Impairment-MCI) participated practicing Mindfulness Based Stress Reduction (Session 2-MBSR) and a novel adaptation of the Kirtan Kriya meditation to the Greek culture setting (Session 3-KK), while a Resting State (RS) condition was undertaken at baseline and follow-up (Session 1-RS Baseline and Session 4-RS Follow-Up). The signals were recorded by using the Muse EEG device and brain waves were computed (alpha, theta, gamma, and beta).
Analysis was conducted on four-electrodes (AF7, AF8, TP9, and TP10). Statistical analysis included the Kruskal-Wallis (KW) nonparametric analysis of variance. The results revealed that both states of MBSR and KK lead to a marked difference in the brain's activation patterns across people at different cognitive states. Wilcoxon Signed-ranks test indicated for HC that theta waves at TP9, TP10 and AF7, AF8 in Session 3-KK were statistically significantly reduced compared to Session 1-RS
= -2.271,
= 0.023,
= -3.110,
= 0.002 and
= -2.341,
= 0.019,
= -2.132,
= 0.033, respectively.
The results showed the potential of the parameters used between the various groups (HC, SCD, and MCI) as well as between the two meditation sessions (MBSR and KK) in discriminating early cognitive decline and brain alterations in a smart-home environment without medical support.
Neuromarketing is a continuously evolving field that utilises neuroimaging technologies to explore consumers' behavioural responses to specific marketing-related stimulation, and furthermore ...introduces novel marketing tools that could complement the traditional ones like questionnaires. In this context, the present paper introduces a multimodal Neuromarketing dataset that encompasses the data from 42 individuals who participated in an advertising brochure-browsing scenario. In more detail, participants were exposed to a series of supermarket brochures (containing various products) and instructed to select the products they intended to buy. The data collected for each individual executing this protocol included: (i) encephalographic (EEG) recordings, (ii) eye tracking (ET) recordings, (iii) questionnaire responses (demographic, profiling and product related questions), and (iv) computer mouse data. NeuMa dataset has both dynamic and multimodal nature and, due to the narrow availability of open relevant datasets, provides new and unique opportunities for researchers in the field to attempt a more holistic approach to neuromarketing.
The classification of Motor Imagery (MI) tasks constitutes one of the most challenging problems in Brain Computer Interfaces (BCI) mostly due to the varying conditions of its operation. These ...conditions may vary with respect to the number of electrodes, the time and effort that can be invested by the user for training/calibrating the system prior to its use, as well as the duration or even the type of the imaginary task that is most convenient for the user. Hence, it is desirable to design classification schemes that are not only accurate in terms of the classification output but also robust to changes in the operational conditions. Towards this goal, we propose a new sparse representation classification scheme that extends current sparse representation schemes by exploiting the group sparsity of relevant features. Based on this scheme each test signal is represented as a linear combination of train trials that are further constrained to belong in the same MI class. Our expectation is that this constrained linear combination exploiting the grouping structure of the training data will lead to representations that are more robust to varying operational conditions. Moreover, in order to avoid overfitting and provide a model with good generalization abilities we adopt the bayesian framework and, in particular, the Variational Bayesian Framework since we use a specific approximate posterior to exploit the grouping structure of the data. We have evaluated the proposed algorithm on two MI datasets using electroencephalograms (EEG) that allowed us to simulate different operational conditions like the number of available channels, the number of training trials, the type of MI tasks, as well as the duration of each trial. Results have shown that the proposed method presents state-of-the-art performance against well known classification methods in MI BCI literature.
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