Fueled by early success stories, the neuromarketing domain advanced rapidly during the last 10 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) stands out as the less invasive and most affordable method. While not equally precise as other neuroimaging technologies in terms of spatial resolution, it can capture brain activity almost at the speed of cognition. Hence, EEG constitutes a favorable candidate for recording and subsequently decoding the consumers' brain activity. However, despite its wide use in neuromarketing, it cannot provide the complete picture alone. In order to overcome the limitations imposed by a single monitoring method, researchers focus on more holistic approaches. The exploitation of hybrid EEG schemes (e.g., combining EEG with eye-tracking, electrodermal activity, heart rate, and/or other) is ever growing and will hopefully allow neuromarketing to uncover consumers' behavior. Our survey revolves around last-decade hybrid neuromarketing schemes that involve EEG as the dominant modality. Beyond covering the relevant literature and state-of-the-art findings, we also provide future directions on the field, present the limitations that accompany each of the commonly employed monitoring methods and briefly discuss the omni-present ethical scepticizm related to neuromarketing.

Gaze-based keyboards offer a flexible way for human-computer interaction in both disabled and able-bodied people. Besides their convenience, they still lead to error-prone human-computer interaction. Eye tracking devices may misinterpret user’s gaze resulting in typesetting errors, especially when operated in fast mode. As a potential remedy, we present a novel error detection system that aggregates the decision from two distinct subsystems, each one dealing with disparate data streams. The first subsystem operates on gaze-related measurements and exploits the eye-transition pattern to flag a typo. The second, is a brain-computer interface that utilizes a neural response, known as Error-Related Potentials (ErrPs), which is inherently generated whenever the subject observes an erroneous action. Based on the experimental data gathered from 10 participants under a spontaneous typesetting scenario, we first demonstrate that ErrP-based Brain Computer Interfaces can be indeed useful in the context of gaze-based typesetting, despite the putative contamination of EEG activity from the eye-movement artefact. Then, we show that the performance of this subsystem can be further improved by considering also the error detection from the gaze-related subsystem. Finally, the proposed bimodal error detection system is shown to significantly reduce the typesetting time in a gaze-based keyboard.

Objective: Spatial covariance matrices are extensively employed as brain activity descriptors in BCI research that, typically, involve the whole array of sensors. Here, we introduce a methodological framework for delineating the subset of sensors, the covariance structure of which offers a reduced, but more powerful, representation of brain's coordination patterns that ultimately leads to reliable mind reading. Methods: Adopting a Riemannian geometry approach, we turn the problem of sensor selection as a maximization of a functional that is computed over the manifold of symmetric positive definite (SPD) matrices and encapsulates class separability in a way that facilitates the search among subsets of different size. The introduced optimization task, namely discriminative covariance reduction (DCR), lacks an analytical solution and is tackled via the Cross-Entropy optimization technique. Results: Based on two different EEG datasets and three distinct classification schemes, we demonstrate that the DCR approach provides a noteworthy gain in terms of accuracy (in some cases exceeding 20%) and a remarkable reduction in classification time (on average 82%). Additionally, results include the intriguing empirical finding that the pattern of selected sensors, in the case of disable persons, depends on the type of disability. Conclusion: The proposed DCR framework can speed up the classification time in BCI-systems operating on the SPD manifolds by simultaneously enhancing their reliability. This is achieved without sacrificing the neuroscientific interpretability endowed in the topographical arrangement of the selected sensors. Significance: Riemannian geometry is exploited for DCR in BCI systems, in a dimensionality-agnostic manner, guaranteeing improved performance.

Electroencephalography signals inherently deviate from the notion of regular spatial sampling, as they reflect the coordinated action from multiple distributed overlapping cortical networks. Hence, the observed brain dynamics are influenced both by the topology of the sensor array and the underlying functional connectivity. Neural engineers are currently exploiting the advances in the domain of graph signal processing in an attempt to create robust and reliable brain decoding systems. In this direction, Geometric Deep Learning is a highly promising concept for combining the benefits of graph signal processing and deep learning towards revolutionising Brain-Computer Interfaces (BCIs). However, its exploitation has been hindered by its data-demanding character. As a remedy, we propose here a novel data augmentation approach that combines the multiplex network modelling of multichannel signal with a graph variant of the classical Empirical Mode Decomposition (EMD), and which proves to be a strong asset when combined with Graph Convolutional Neural Networks (GCNNs). As our graph-EMD algorithm makes no assumptions with respect to linearity and stationarity, it appears as an appealing solution towards analysing brain signals without artificially imposing regularities in either temporal or spatial domain. Our experimental results indicate that the proposed scheme for data augmentation leads to substantial improvement when it is combined with GCNNs. Using recordings from two distinct BCI applications and comparing against a state-of-the-art augmentation method, we illustrate the benefits from its use. By making it available to BCI community, we hope to further foster the application of geometric deep learning in the field.

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).

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 collected from 34 individuals (18 able-bodied and 16 motor-impaired). Data were collected during the interaction with specifically designed interface for web browsing and multimedia content manipulation and during imaginary movement tasks. 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.