Towards EEG-based BCI driven by emotions for addressing BCI-Illiteracy: a meta-analytic review

Many critical aspects affect the correct operation of a Brain Computer Interface. The term BCI-illiteracy' describes the impossibility of using a BCI paradigm. At present, a universal solution does not exist and seeking innovative protocols to drive a BCI is mandatory. This work presents a meta-analytic review on recent advances in emotions recognition with the perspective of using emotions as voluntary, stimulus-independent, commands for BCIs. 60 papers, based on electroencephalography measurements, were selected to evaluate what emotions have been most recognised and what brain regions were activated by them. It was found that happiness, sadness, anger and calm were the most recognised emotions. Relevant discriminant locations for emotions recognition and for the particular case of discrete emotions recognition were identified in the temporal, frontal and parietal areas. The meta-analysis was mainly performed on stimulus-elicited emotions, due to the limited amount of literature about self-induced emotions. The obtained results represent a good starting point for the development of BCI driven by emotions and allow to: (1) ascertain that emotions are measurable and recognisable one from another (2) select a subset of most recognisable emotions and the corresponding active brain regions

Optimizing Nozzle Travel Time in Proton Therapy

Proton therapy is a cancer therapy that is more expensive than classical radiotherapy but that is considered the gold standard in several situations. Since there is also a limited amount of delivering facilities for this techniques, it is fundamental to increase the number of treated patients over time. The objective of this work is to offer an insight on the problem of the optimization of the part of the delivery time of a treatment plan that relates to the movements of the system. We denote it as the Nozzle Travel Time Problem (NTTP), in analogy with the Leaf Travel Time Problem (LTTP) in classical radiotherapy. In particular this work: (i) describes a mathematical model for the delivery system and formalize the optimization problem for finding the optimal sequence of movements of the system (nozzle and bed) that satisfies the covering of the prescribed irradiation directions; (ii) provides an optimization pipeline that solves the problem for instances with an amount of irradiation directions much greater than those usually employed in the clinical practice; (iii) reports preliminary results about the effects of employing two different resolution strategies within the aforementioned pipeline, that rely on an exact Traveling Salesman Problem (TSP) solver, Concorde, and an efficient Vehicle Routing Problem (VRP) heuristic, VROOM

トクシュウ トクシマケン ノ キュウキュウ イリョウ ト チイキ イリョウ : ゲンジョウ ト テンボウ : カントウゲン

We introduce GFrames, a novel local reference frame (LRF) construction for 3D meshes and point clouds. GFrames are based on the computation of the intrinsic gradient of a scalar field defined on top of the input shape. The resulting tangent vector field defines a repeatable tangent direction of the local frame at each point; importantly, it directly inherits the properties and invariance classes of the underlying scalar function, making it remarkably robust under strong sampling artifacts, vertex noise, as well as non-rigid deformations. Existing local descriptors can directly benefit from our repeatable frames, as we showcase in a selection of 3D vision and shape analysis applications where we demonstrate state-of-the-art performance in a variety of challenging settings

Subject - specific - frequency - band for motor imagery EEG signal recognition based on common spatial spectral pattern

Over the last decade, processing of biomedical signals using machine learning algorithms has gained widespread attention. Amongst these, one of the most important signals is electroencephalography (EEG) signal that is used to monitor the brain activities. Brain-computer-interface (BCI) has also become a hot topic of research where EEG signals are usually acquired using non-invasive sensors. In this work, we propose a scheme based on common spatial spectral pattern (CSSP) and optimization of temporal filters for improved motor imagery (MI) EEG signal recognition. CSSP is proposed as it improves the spatial resolution while the temporal filter is optimized for each subject as the frequency band which contains most significant information varies amongst different subjects. The proposed scheme is evaluated using two publicly available datasets: BCI competition III dataset IVa and BCI competition IV dataset 1. The proposed scheme obtained promising results and outperformed other state-of-the-art methods. The findings of this work will be beneficial for developing improved BCI systems

A Deep Learning Approach for Mood Recognition from Wearable Data

Emotion and mood recognition plays a key role in human-robot interaction, especially in the context of socially assistive robotics. Mood-aware robots could be useful as companions and social assistants for elders and people affected by depression and other mood disorders. An interesting option for continuously tracking a user's mood is the use of wearable and mobile devices. However, the classification of the mood from physiological and kinematics data is still a challenge, due to intersubjects differences: on one hand, 'one-fits-all' classification approaches usually achieve lower accuracy than person-specific methods; on the other hand, personalized models require in general a large amount of data from a single subject to be trained and, therefore, becomes effective after long periods of acquisition. In this paper, we propose a deep learning approach for mood recognition from a publicly available dataset that includes a gyroscope, accelerometer, and heart-rate data. We propose the use of long-short term memory networks (LSTM), testing them both as classifiers and as features extractors in hybrid models. We compared their performances both against and in conjunction with traditional machine learning approaches, namely support vector machines (SVM) and Gaussian mixture models (GMM). We also consider transfer learning strategies to reduce the amount of personal data needed to train the model. Our results show that the use of LSTMs significantly improves the classification accuracy with respect to machine learning approaches, especially if employed as feature extractors and combined with SVM. However, we observed that transfer learning does not achieve significant results in boosting the training of a personalized model

On-line Avoidance of Communication Intrusion in Token Ring Networks

Distributed programs are often instrumented for collecting information to assist in analyzing the behavior of an application. However, the act of monitoring a process can alter the very behavior being monitored. The changes in the behavior occur because the order in which application messages are delivered at a site and the pool of messages available at the site at critical execution points are different for monitored and unmonitored executions. We consider a distributed system in which communication is carried out using a token ring network and the scheduling of the application processes mapped to a site is carried out using priority based scheduling. In this paper we present intrusion removal techniques that control the relative progress in communication and computation activities to avoid changes in message ordering and message pool contents at each site. Keywords - monitoring, intrusion, token ring protocol, message passing, priority scheduling. 1 Introduction Programs written i..