Affective state classification through CMAC-based model of affects (CCMA) using SVM

A number of computational models have been proposed to perform emotion profiling through affective state classification using EEG signals. However, such models do not include both temporal and spatial dynamic of the signals. It is also observed that the performance of classifying emotion using the existing models produce high classification accuracy on one subject, but not on different subjects. Thus, in this paper CMAC-based Computational Model of Affects (CCMA) is proposed as feature extraction for the classification task. CCMA keeps the temporal and spatial dynamics of EEG signals to produce better classification performance. Using Support Vector Machine (SVM) as classifier, the features produce higher classification accuracy for heterogeneous test

Conceptual Blending in Biomusic Composition Space: The "Brainswarm" Paradigm

(Abstract to follow

Preference Classification Using Electroencephalography (EEG) and Deep Learning

Electroencephalogram (EEG)-based emotion classification is rapidly becoming one of the most intensely studied areas of brain-computer interfacing (BCI). The ability to passively identify yet accurately correlate brainwaves with our immediate emotions opens up truly meaningful and previously unattainable human-computer interactions such as in forensic neuroscience, rehabilitative medicine, affective entertainment and neuro-marketing. One particularly useful yet rarely explored areas of EEG-based emotion classification is preference recognition [1], which is simply the detection of like versus dislike. Within the limited investigations into preference classification, all reported studies were based on musicallyinduced stimuli except for a single study which used 2D images. We present two EEG-based preference classification studies: using (1) kNN for a 10-subject EEG classification problem; (2) deep learning for an expanded 16-subject EEG classification problem. We show that inter-subject variability introduces significant classification problems when larger cohorts of test subjects are used and that deep learning shows promising results in terms of addressing this inter-subject variability problem in EEG-based preference classification

Discrete classification technique applied to TV advertisements liking recognition system based on low‑cost EEG headsets

Background: In this paper a new approach is applied to the area of marketing research. The aim of this paper is to recognize how brain activity responds during the visualization of short video advertisements using discrete classification techniques. By means of low cost electroencephalography devices (EEG), the activation level of some brain regions have been studied while the ads are shown to users. We may wonder about how useful is the use of neuroscience knowledge in marketing, or what could provide neuroscience to marketing sector, or why this approach can improve the accuracy and the final user acceptance compared to other works. Methods: By using discrete techniques over EEG frequency bands of a generated dataset, C4.5, ANN and the new recognition system based on Ameva, a discretization algorithm, is applied to obtain the score given by subjects to each TV ad. Results: The proposed technique allows to reach more than 75 % of accuracy, which is an excellent result taking into account the typology of EEG sensors used in this work. Furthermore, the time consumption of the algorithm proposed is reduced up to 30 % compared to other techniques presented in this paper. Conclusions: This bring about a battery lifetime improvement on the devices where the algorithm is running, extending the experience in the ubiquitous context where the new approach has been tested.Ministerio de Economía y Competitividad HERMES TIN2013-46801-C4-1-rJunta de Andalucia Simon TIC-805

EEG Analysis of Person Familiarity with Audio-Video Data Assessing Task

To solve the problem of assessing a person’s familiarity with audio-video data, various methods of machine learning were compared. The feature space has been optimized for the best way to make such an assessment. The high efficiency of the genetic algorithm in the problem of optimizing the space of attributes is shown.Мета статті. Виконати порівняльний аналіз і експериментальне дослідження ефективності різних методів машинного навчання для побудови моделі визначення знайомства з аудіовізуальними матеріалами, на основі аналізу сигналу електроенцефалограм і визначити набір ознак, які найкраще класифікують даний сигнал. Результат. За використання запропонованої інформаційної технології підібрано параметри і отримано результати точності для різних моделей класифікації, що дозволило порівняти такі моделі і визначити найбільш адекватні до вирішення поставленої задачі. Застосування методів відбору ознак дозволило підвищити точність моделі лінійного методу опорних векторів з 55,9 до 80,7 відсотків.Цель статьи. Выполнить сравнительный анализ и экспериментальное исследование эффективности различных методов машинного обучения для построения модели определения знакомства с представленными аудиовизуальными материалами, на основе анализа сигнала электроэнцефалограмм и определить набор признаков, наилучшим образом классифицирующих данный сигнал. Результат. С использованием предложенной информационной технологии, подобраны параметры и получены результаты точности для различных моделей классификации, что позволило сравнить такие модели и определить наиболее адекватные решению поставленной задачи. Применение методов отбора признаков позволило повысить точность модели линейного метода опорных векторов с 55,9 до 80,7 процентов

EEG Analysis during Music Perception

This review presents the most interesting results of electroencephalographic studies on musical perception performed with different analysis techniques. In first place, concepts on intra-musical characteristics such as tonality, rhythm, dissonance or musical syntax, which have been object of further investigation, are introduced. Most of the studies found use listening musical extracts, sequences of notes or chords as an experimental situation, with the participants in a resting situation. There are few works with participants performing or imagining musical performance. The reviewed works have been divided into two groups: a) those that analyze the EEGs recorded in different cortical areas separately using frequency domain techniques: spectral power, phase or time domain EEG procedures such as potentials event related (ERP); b) those that investigate the interdependence between different EEG channels to evaluate the functional connectivity between different cortical areas through different statistical or synchronization indices. Most of the aspects studied in music-brain interaction are those related to musical emotions, syntax of different musical styles, musical expectation, differences between pleasant and unpleasant music and effects of musical familiarity and musical experience. Most of the works try to know the topographic maps of the brain centers, pathways and functions involved in these aspects

A multi-genre model for music emotion recognition using linear regressors

Making the link between human emotion and music is challenging. Our aim was to produce an efficient system that emotionally rates songs from multiple genres. To achieve this, we employed a series of online self-report studies, utilising Russell's circumplex model. The first study (n = 44) identified audio features that map to arousal and valence for 20 songs. From this, we constructed a set of linear regressors. The second study (n = 158) measured the efficacy of our system, utilising 40 new songs to create a ground truth. Results show our approach may be effective at emotionally rating music, particularly in the prediction of valence

Investigation of EEG-based indicators of skill acquisition as novice participants practice a lifeboat manoeuvering task in a simulator

Adequate training is essential in safety critical occupations. Task proficiency is typically assessed through relevant performance measures. While such measures provide information about how effectively an individual can perform the task, they give no insight about their comfort level. Ideally, individuals would be capable of executing tasks not just at a certain level of performance, but also with confidence and a high degree of cognitive efficiency. Neural signals may provide information regarding a trainee’s task proficiency that performance measures alone cannot. The purpose of this study was to investigate patterns in neural activity that are indicative of task proficiency. Ten novice participants completed ten trials of a manoeuvering task in a high-fidelity lifeboat simulator while their neural activity was recorded via 64-channel EEG. Power spectral features were used along with linear discriminant analysis to classify the data from pairs of consecutive trials. Repeated measures mixed model linear regression showed that on average, the classification accuracy of consecutive trials decreased significantly over the course of training (from 82% to 73%). Since the classification accuracies reflect how different the neural activation patterns in the brain are between the trials classified, this result indicates that with practice, the associated neural activity becomes more similar from trial to trial. We hypothesize that in the early stages of the practice session, the neural activity is quite distinct from trial to trial as the individual works to develop and refine a strategy for task execution, then as they settle on an effective strategy, their neural activity becomes more stable across trials, explaining the lower classification accuracy observed in consecutive trials later in the session. These results could be used to develop a neural indicator of task proficiency