19 research outputs found
A Consumer BCI for Automated Music Evaluation Within a Popular On-Demand Music Streaming Service “Taking Listener’s Brainwaves to Extremes”
No full textPart 9: Artificial Neural Network Modeling (ANNMO)International audienceWe investigated the possibility of a using a machine-learning scheme in conjunction with commercial wearable EEG-devices for translating listener’s subjective experience of music into scores that can be used for the automated annotation of music in popular on-demand streaming services.Based on the established -neuroscientifically sound- concepts of brainwave frequency bands, activation asymmetry index and cross-frequency-coupling (CFC), we introduce a Brain Computer Interface (BCI) system that automatically assigns a rating score to the listened song.Our research operated in two distinct stages: (i) a generic feature engineering stage, in which features from signal-analytics were ranked and selected based on their ability to associate music induced perturbations in brainwaves with listener’s appraisal of music. (ii) a personalization stage, during which the efficiency of extreme learning machines (ELMs) is exploited so as to translate the derived patterns into a listener’s score. Encouraging experimental results, from a pragmatic use of the system, are presented
A Multimodal dataset for authoring and editing multimedia content: The MAMEM project
No full textIn this report we present a dataset that combines multimodal biosignals and eye tracking information gathered under a human-computer interaction framework. The dataset was developed in the vein of the MAMEM project that aims to endow people with motor disabilities with the ability to edit and author multimedia content through mental commands and gaze activity. The dataset includes EEG, eye-tracking, and physiological (GSR and Heart rate) signals along with demographic, clinical and behavioral data collected from 36 individuals (18 able-bodied and 18 motor-impaired). Data were collected during the interaction with specifically designed interface for web browsing and multimedia content manipulation and during imaginary movement tasks. Alongside these data we also include evaluation reports both from the subjects and the experimenters as far as the experimental procedure andcollected dataset are concerned. We believe that the presented dataset will contribute towards the development and evaluation of modern human-computer interaction systems that would foster the integration of people with severe motor impairments back into society
Error Related Potentials from Gaze-Based Typesetting
No full textThe recording protocol relied on a standard gaze-based keyboard paradigm that was implemented by an eye-tracker attached to a PC monitor. The gazing information, in the form of a densely sampled sequence of x-y coordinates corresponding to the eye trace on the screen, was registered simultaneously with the participant’s brainwaves. The purpose of this experiment was to provide data where patterns in the physiological activity, of either brain or eyes, could be associated with the case of a typo (due to either the inaccuracy of the eye-tracker or a human mistake)
A Collaborative Representation Approach to Detecting Error-Related Potentials in SSVEP-BCIs
No full textThis study takes advantage of Error Related Potentials, a certain type of neurophysiological event associated with humans’ ability to observe and recognize erroneous actions, in order to improve SSVEP-based Brain Computer Interfaces (BCIs). The Error Related Potentials serve as a passive correction mechanism that originates directly from the user’s brain. In this paper we propose a novel approach to spatial filtering, based on a supervised variant of Collaborative Representation Projections (CRP) offering a more discriminant representation of electroencephalography signals for detecting Error Related Potentials. This new approach enhances the detectability of Error Related Potentials by projecting the spatial information of signals into a new space where samples of the same class tend to form local neighborhoods. Moreover, the limitations under which the Error Related Potentials positively contribute to the performance of a SSVEP-based BCI are explored. For this reason we also provide a new methodology, namely Inverse Correct Response Time (ICRT), that reliably captures the trade-off, between the gain of the automated error detection and the induced time delay of a BCI system that potentially incorporates Error Related Potentials
An Error Aware SSVEP-based BCI
No full textError-Related Potentials (ErrPs) have been used lately in order to improve several existing Brain-Computer Interface (BCI) applications. In our study we investigate the contribution of ErrPs in a Steady State Visual Evoked Potential (SSVEP) based BCI. An extensive study is presented in order to discover the limitations of the proposed scheme. Using Common Spatial Patterns and Random Forests we manage to show encouraging results regarding the incorporation of ErrPs in a SSVEP system. Finally, we provide a novel methodology (Inverse Correct Response Time) that can measure the gain of a BCI system by incorporating ErrPs in terms of time efficiency
The Visual Saliency Transformer Goes Temporal: TempVST for Video Saliency Prediction
No full textThe Transformer revolutionized Natural Language Processing and Computer Vision by effectively capturing contextual relationships in sequential data through its attention mechanism. While Transformers have been explored sufficiently in traditional computer vision tasks such as image classification, their application to more intricate tasks, such as Video Saliency Prediction (VSP), remains limited. Video saliency prediction is the task of identifying the most visually salient regions in a video, which are likely to capture a viewer’s attention. In this study, we propose a pure transformer architecture named Temporal Visual Saliency Transformer (TempVST) for the VSP task. Our model leverages the Visual Saliency Transformer (VST) as a backbone, with the addition of a Transformer-based temporal module that can seamlessly transition diverse architectural frameworks from image to video domain, through the incorporation of temporal recurrences. Moreover, we demonstrate that transfer learning is viable in the context of VSP through Transformer architectures and helps reduce the duration of the training phase, leading to a reduction in the duration of the training phase by 41% and 45% in two different datasets. Our experiments were conducted on two benchmark datasets, DHF1K and LEDOV, and our results show that our network can compete with all other state-of-the-art models
A Sparse Representation Classification Scheme for the Recognition of Affective and Cognitive Brain Processes in Neuromarketing
No full textIn 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)
e-Vision: An AI-powered system for promoting the autonomy of visually impaired
No full textComputer vision-based assistive technology for the visually impaired is still a field of ongoing research. Its fundamental scope is to extend the frontiers of visually impaired by means of providing a greater degree of independence and autonomy in their daily living activities. Towards this direction, we present the “e-Vision”, a hybrid system that couples the convenience and the inherently seamless adoption of an external camera embedded within a pair of eyeglasses with the processing power of modern smartphone devices. The system consists of a pair of eyeglasses integrating a camera and a mobile application that encapsulates computer vision algorithms capable to enhance several daily living tasks for the visually impaired. The proposed system is a context-aware solution and builds upon three important day-to-day activities: visiting a super-market, going an outdoor walk and carrying out a work at a public service. Going one step further, the e-Vision also caters for social inclusion by providing social context and enhances overall experience by adopting soundscapes that allow users to perceive selected points of interest in an immersive acoustic way
