EEG based word familiarity using features and frequency bands combination

Khurana, Vaishali; Kumar, Pradeep; Saini, Rajkumar; Roy, Partha Pratim

doi:10.1016/j.cogsys.2017.11.003

Cited by 23 publications

(4 citation statements)

References 28 publications

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“…Their fixity is evident mainly in the zero article before the noun (zu Hause 'at home') or a fixed combination of the preposition and article im Allgemeinen 'in general'. Recurrent zero articles between the preposition and the noun themselves signal a lexicalisation and phraseologisation of these word combinations (Khurana et al, 2018). In the causal combinations analysed here, the zero article is also conditioned by the nature of the noun and this is abstract.…”

Section: Preposition-noun Phrasesmentioning

confidence: 75%

Corpus based analysis of established preposition-noun word combinations with the meaning of emotions (aus Spaß = for fun, aus Lust = for pleasure, aus Angst = from fear...)

et al. 2021

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Corpus linguistics is used in this paper to describe selected established German-language combinations firmly connecting prepositions and nouns (German: Substantiv) with a zero article, a sign of their lexicalisation. The focus is on preposition-noun combinations that express emotions in a primarily causal adverbial function, such as aus Angst ‘from fear’, aus Furcht ‘for fear’; aus Spaß ‘for fun’, aus Lust ‘for pleasure’ aus Verzweiflung ‘out of desperation/despair’, aus Frust ‘out of frustration’. The paper points out the insufficient lexicographic description currently available in dictionaries, while emphasising that the meaning of the word combination can only be analysed in context and, from their typical occurrence, it is possible to distinguish several aspects of the combination used, something dictionaries usually do not reflect. Research into these word combinations constitute a desideration of contemporary linguistics.

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Section: Preposition-noun Phrasesmentioning

confidence: 75%

Corpus based analysis of established preposition-noun word combinations with the meaning of emotions (aus Spaß = for fun, aus Lust = for pleasure, aus Angst = from fear...)

et al. 2021

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“…Neuroscience-based marketing has made substantial strides in understanding consumer behavior over the past decade [48]. This study contributes to the field by proposing a robust EEG-based neuromarketing framework that combines DTL, spatial attention methods, and deep neural networks.…”

Section: Discussionmentioning

confidence: 99%

Spatial Attention-Enhanced EEG Analysis for Profiling Consumer Choices

Panda,

Chakladar,

Rana

et al. 2024

IEEE Access

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Over the years, research in neuroscience-driven marketing has progressively delved into the conscious and subconscious behaviors of consumers. Existing Electroencephalography (EEG)-based studies related to consumer preferences toward products are not comprehensive. Due to non-stationarity issues of EEG, a significant variance is observed in inter-trial and inter-session EEG signals of a subject, which leads to challenges in building a universal consumer preference model across diverse subjects, sessions, and tasks. Transfer learning mitigates this challenge by utilizing data or knowledge from similar subjects, sessions, or tasks to improve the learning process for a new subject, session, or task, thereby enhancing overall model performance. Moreover, high-dimensional EEG features often lead to poor classification results. Therefore, selecting meaningful or refined features is of utmost importance for classification. Therefore, we propose a robust EEG-based neuromarketing framework combining deep transfer learning, spatial attention model, and deep neural networks. The proposed framework predicts the consumer choices (in terms of "likes" and "dislikes") for e-commerce products. Initially, the knowledge distillation is performed from the pre-trained network to the proposed model, and the model is trained on the connectivity features of EEG. Next, the attention-based features are extracted from high-level connectivity features using the spatial attention model (Convolutional Block Attention Module: CBAM). CBAM extracts the attention feature maps along channel and spatial dimensions for adaptive feature refinement. The refined features improve the classification accuracy. Finally, the attention-based features are passed to the 2D CNN-based deep learning model to evaluate consumer choices. The proposed model achieves 95.60% classification accuracy with the experimental dataset. The proposed model achieves a significant improvement of 2.60% over the existing neuromarketing-based studies.

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“…Various studies have found wavelet transform as a more efficient method for spectral analysis. 26 , 27 Daubechies (Db)-8 wavelet transform is used here 28 . A wavelet function at time t is shown in Eqs.…”

Section: Methodsmentioning

confidence: 99%

Decoding the cognitive states of attention and distraction in a real-life setting using EEG

Kaushik

Moye

Vugt

et al. 2022

Sci Rep

Self Cite

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Lapses in attention can have serious consequences in situations such as driving a car, hence there is considerable interest in tracking it using neural measures. However, as most of these studies have been done in highly controlled and artificial laboratory settings, we want to explore whether it is also possible to determine attention and distraction using electroencephalogram (EEG) data collected in a natural setting using machine/deep learning. 24 participants volunteered for the study. Data were collected from pairs of participants simultaneously while they engaged in Tibetan Monastic debate, a practice that is interesting because it is a real-life situation that generates substantial variability in attention states. We found that attention was on average associated with increased left frontal alpha, increased left parietal theta, and decreased central delta compared to distraction. In an attempt to predict attention and distraction, we found that a Long Short Term Memory model classified attention and distraction with maximum accuracy of 95.86% and 95.4% corresponding to delta and theta waves respectively. This study demonstrates that EEG data collected in a real-life setting can be used to predict attention states in participants with good accuracy, opening doors for developing Brain-Computer Interfaces that track attention in real-time using data extracted in daily life settings, rendering them much more usable.

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EEG based word familiarity using features and frequency bands combination

Cited by 23 publications

References 28 publications

Corpus based analysis of established preposition-noun word combinations with the meaning of emotions (aus Spaß = for fun, aus Lust = for pleasure, aus Angst = from fear...)

Corpus based analysis of established preposition-noun word combinations with the meaning of emotions (aus Spaß = for fun, aus Lust = for pleasure, aus Angst = from fear...)

Spatial Attention-Enhanced EEG Analysis for Profiling Consumer Choices

Decoding the cognitive states of attention and distraction in a real-life setting using EEG

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