Companies need to ensure that customers perceive their brands as intended, with strong and unique associations, when facing a competitive market. Traditionally, brand associations are measured using conventional techniques such as surveys and questionnaires albeit both conscious and unconscious factors can influence the collected data and the outcome of a campaign. Neuromarketing can shed light on how the customer’s brain processes marketing stimuli. We report here on an EEG study aimed at gauging mental associations with brands. We focus on the N400 event-related potential, an EEG component most strongly elicited in response to a concept unrelated to a preceding concept. We considered two video on demand brands, Netflix and Rex&Rio , and selected a set of words grouped in 4 categories that were either related ( Television , Relaxation , and Price ), in varying degrees, or unrelated ( Unrelated ) to the said brands. The experiment started with both brands’ TV commercials, as a common reference for our participants. We then applied a semantic priming paradigm in which a brand logo (“prime”) was followed by a word (“target”), and the strength of the N400 response to the word used as an inverted measure of the association strength with the brand logo. We clustered N400 responses to identify, for each brand, natural groups of associated words. As a result, for Netflix the cluster with the smallest N400 responses (i.e., strongest associations) consisted of words related to Television but for Rex&Rio it consisted of words related to Relaxation . We also evaluated the relationship between the two brands and determined which associations they share or which ones not. It turned out that associations related to Relaxation and Television distinguish the two brands. Interestingly, survey data did not show any difference between the two brands as they were equally associated with Television and Relaxation . These findings show that our N400 technique can reveal brand associations, and natural categories thereof, that would otherwise go unnoticed when using conventional surveys.
Lexical access in bilinguals has been considered either selective or non-selective and evidence exists in favor of both hypotheses. We conducted a linguistic experiment to assess whether a bilingual’s language mode influences the processing of first language information. We recorded event related potentials during a semantic priming paradigm with a covert manipulation of the second language (L2) using two types of stimulus presentations (short and long). We observed a significant facilitation of word pairs related in L2 in the short version reflected by a decrease in N400 amplitude in response to target words related to the English meaning of an inter-lingual homograph (homograph-unrelated group). This was absent in the long version, as the N400 amplitude for this group was similar to the one for the control-unrelated group. We also interviewed the participants whether they were aware of the importance of L2 in the experiment. We conclude that subjects participating in the long and short versions were in different language modes: closer to monolingual mode for the long and closer to bilingual mode for the short version; and that awareness about covert manipulation of L2 can influence the language mode, which in its turn influences the processing of the first language.
We report on a unique electrocorticography (ECoG) experiment in which Steady-State Visual Evoked Potentials (SSVEPs) to frequency- and phase-tagged stimuli were recorded from a large subdural grid covering the entire right occipital cortex of a human subject. The paradigm is popular in EEG-based Brain Computer Interfacing where selectable targets are encoded by different frequency- and/or phase-tagged stimuli. We compare the performance of two state-of-the-art SSVEP decoders on both ECoG- and scalp-recorded EEG signals, and show that ECoG-based decoding is more accurate for very short stimulation lengths (i.e., less than 1 s). Furthermore, whereas the accuracy of scalp-EEG decoding benefits from a multi-electrode approach, to address interfering EEG responses and noise, ECoG decoding enjoys only a marginal improvement as even a single electrode, placed over the posterior part of the primary visual cortex, seems to suffice. This study shows, for the first time, that EEG-based SSVEP decoders can in principle be applied to ECoG, and can be expected to yield faster decoding speeds using less electrodes.
Multiway array decomposition has been successful in providing a better understanding of the structure underlying data and in discovering potentially hidden feature dependencies serving high-performance decoder applications. However, the computational cost of multi-way algorithms can become prohibitive, especially when considering large data sets, rendering them unsuitable for time-critical applications. We propose a multi-way regression model for large-scale tensors with optimized performance in terms of time complexity, called fast Higher-Order Partial Least Squares (fHOPLS). We compare fHOPLS with its native version, Higher-Order Partial Least Squares (HOPLS), the state-of-the-art in multilinear regression, under different noise conditions and tensor dimensionalities using synthetic data. We also compare their performance when used for predicting scalp-recorded electroencephalography (EEG) signals from invasively-recorded electrocorticography (ECoG) signals in an oddball experiment. For the sake of exposition, we evaluated the performance of standard unfolded Partial Least Squares- (PLS) and linear regression. Our results show that fHOPLS is significantly faster than HOPLS in particular for big data. In addition, the regression performances of fHOPLS and HOPLS are comparable and outperform both unfolded PLS and linear regression. Another interesting result is that multiway array decoding yields more accurate results than epoch-based averaging procedures traditionally used in the brain computer interfacing (BCI) community.
Objective: in this work, we aim to develop a more efficient visual motion-onset based Brain-computer interface (BCI). Brain-computer interfaces provide communication facilities that do not rely on the brain's usual pathways. Visual BCIs are based on changes in EEG activity in response to attended flashing or flickering targets. A less taxing way to encode such targets is with briefly moving stimuli, the onset of which elicits a lateralized EEG potential over the parieto-occipital scalp area called the motion-onset visual evoked potential (mVEP). Methods: We recruited 21 healthy subjects for an experiment in which motion-onset stimulations translating leftwards (LT) or rightwards (RT) were encoding 9 displayed targets.We propose a novel algorithm that exploits the phase-shift between EEG electrodes to improve target decoding performance. We hereto extend the spatiotemporal beamformer (stBF) with a phase extracting procedure, leading to the phase-spatial beamformer (psBF). Results: we show that psBF performs significantly better than the stBF (p < 0.001 for 1 and 2 stimulus repetitions and p < 0.01 for 3 to 5 stimulus repetitions), as well as the previously validated linear support-vector machines (p < 0.001 for 5 stimulus
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