| 1. |
Reconstructing EOG From EEG Timeseries: A Spatial Filtering Approach Unobtrusive mental state monitoring based on neurosphysiological signals has seen thriving developments over the past decade, with a wide area of applications, from rehabilitation to neuroergonomics and neuromarketing. Particularly, electroencephalography (EEG) and electrooculography (EOG) have been popular techniques to obtain cognitiverelevant biosignals. However, current wearable systems may still pose practical inconvenience, motivating further interest to integrate EOG+EEG recording into streamlined |
| 2. |
Sparse Graph-based Representations of SSVEP Responses Under the Variational Bayesian Framework The recognition of Steady State Visual Evoked Potentials (SSVEP) constitutes a challenging problem in Brain Computer Interfaces (BCI), especially when the number of EEG sensors is limited. In this work, we propose a new sparse representation classification scheme that extends current schemes by exploiting the graph properties of relevant features. Based on this scheme each test signal is represented as |
| 3. |
Fueled by early success stories, the neuromarketing domain advanced rapidly during the last ten years. As exciting new techniques were being adapted from medical research to the commercial domain, many neuroscientists and marketing practitioners have taken the chance to exploit them so as to uncover the answers of the most important marketing questions. Among the available neuroimaging technologies, electroencephalography (EEG) |
| 4. |
RNeuMark: A Riemannian EEG Analysis Framework for Neuromarketing Neuromarketing exploits neuroimaging techniques so as to reinforce the predictive power of conventional marketing tools, like questionnaires and focus groups. Electroencephalography (EEG) is the most commonly encountered neuroimaging technique due to its non-invasiveness, low-cost, and its very recent embedding in wearable devices. The transcription of brainwave patterns to consumer attitude is supported by various signal descriptors, while the quest for |
| 5. |
Revisiting Riemannian Geometry-based EEG decoding through Approximate Joint Diagonalization The wider adoption of Riemannian geometry in EEG processing is hindered by two factors: i) it involves the manipulation of complex mathematical formulations and, ii) it leads to computationally demanding tasks. The main scope of this work is to simplify particular notions of Riemannian geometry and provide an efficient and comprehensible scheme for neuroscientific explorations. To overcome the aforementioned shortcomings, |
| 6. |
Reconstructing EOG From EEG Timeseries: A Spatial Filtering Approach Unobtrusive mental state monitoring based on neurosphysiological signals has seen thriving developments over the past decade, with a wide area of applications, from rehabilitation to neuroergonomics and neuromarketing. Particularly, electroencephalography (EEG) and electrooculography (EOG) have been popular techniques to obtain cognitive-relevant biosignals. However, current wearable systems may still pose practical inconvenience, motivating further interest to integrate EOG+EEG recording into streamlined |
| 7. |
The objective of this article is to evaluate whether neuromarketing tools, like eye trackers and electroegephalography (EEG), in the form of an integrated software system (NeuroMkt) can be used by small-medium marketing agencies to evaluate their clients’ marketing material. Results that are based on physiological responses are more valid compared to results that are based on customers’ self-reported data. However, |
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