Source analysis in motor imagery EEG BCI applications

Zaitcev, Aleksandr; Liu, Wei; Cook, G.G.; Paley, Martyn N.J.; Milne, Elizabeth

doi:10.1049/pbhe016e_ch7

Cited by 2 publications

(2 citation statements)

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“…EEG source localization seeks to identify the primary cortical current sources generating the measured scalp potentials [31], [32]. This approach overcomes the limitations of electrode cross-correlation due to volume conduction effects [32]. The localization pipeline comprises of forward and inverse problem solving.…”

Section: Sensor To Source Mappingmentioning

confidence: 99%

Dementia Severity Index: A Threshold-Based Approach to Classifying Dementia Level

Ranjan,

Kumar

2024

Preprint

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Dementia is marked by a progressive decline in cognitive and emotional capacities, presenting significant challenges to daily functioning. This includes prominent neurodegenerative disorders like Alzheimer's disease (AD) and Frontotemporal dementia (FTD). Recent advancements in electroencephalogram (EEG) sensors and processing tools, project it as a potential biomarker for detecting neuronal and cognitive changes associated with various dementia types. Investigations related to characterization and differentiation are yet to be explored for range identification and assigning quantitative values to resting state EEGs from different dementia conditions. In this study, two features that capture the band-specific alterations are computed for each subject. These attributes formed the basis of the Dementia Severity Index (DSI), a threshold-based methodology designed to categorize individuals into AD, FTD, and HC from the resting EEG. The introduced thresholding technique underwent validation using machine learning methodologies, specifically the k-nearest neighbors algorithm (kNN) and random forest (RF), achieving accuracies of 81.6% and 81.37%, respectively. The classification outcomes of derived DSI from F1 and F2 are compared. The $DSI$ corresponding to significant feature F1 is validated on two diverse EEG datasets. The study aims to contribute to the field by providing a set of dementia indexes capable of distinguishing between AD and FTD-based dementia and discriminating against HC. Additionally, the ability of significant features to reflect cognitive performance is explored using the Spearman correlation coefficient (r) to quantify the relationship between predicted Mini-Mental State Examination (MMSE) and actual MMSE scores. The study also delves into the variations in sensor and source domain classification using features F1 and F2. The findings of the proposed approach hold promise for capturing a range of values that can effectively classify AD, FTD, and HC, while also offering the advantage of computationally efficient classification when compared with the existing subjective assessment.

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Section: Sensor To Source Mappingmentioning

confidence: 99%

Dementia Severity Index: A Threshold-Based Approach to Classifying Dementia Level

Ranjan,

Kumar

2024

Preprint

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“…EEG through the measurement of electrical potentials on the scalp, can reflect the activity of different brain regions and plays a crucial role in understanding brain function ( Zaitcev et al, 2017 ; Bhavsar, 2019 ). As the cost of EEG equipment decreases and data acquisition becomes easier, the application of EEG is becoming more widespread ( Soufineyestani et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Assigning channel weights using an attention mechanism: an EEG interpolation algorithm

Liu,

Wang,

Qiu

et al. 2023

Front. Neurosci.

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During the acquisition of electroencephalographic (EEG) signals, various factors can influence the data and lead to the presence of one or multiple bad channels. Bad channel interpolation is the use of good channels data to reconstruct bad channel, thereby maintaining the original dimensions of the data for subsequent analysis tasks. The mainstream interpolation algorithm assigns weights to channels based on the physical distance of the electrodes and does not take into account the effect of physiological factors on the EEG signal. The algorithm proposed in this study utilizes an attention mechanism to allocate channel weights (AMACW). The model gets the correlation among channels by learning from good channel data. Interpolation assigns weights based on learned correlations without the need for electrode location information, solving the difficulty that traditional methods cannot interpolate bad channels at unknown locations. To avoid an overly concentrated weight distribution of the model when generating data, we designed the channel masking (CM). This method spreads attention and allows the model to utilize data from multiple channels. We evaluate the reconstruction performance of the model using EEG data with 1 to 5 bad channels. With EEGLAB’s interpolation method as a performance reference, tests have shown that the AMACW models can effectively reconstruct bad channels.

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Source analysis in motor imagery EEG BCI applications

Cited by 2 publications

References 0 publications

Dementia Severity Index: A Threshold-Based Approach to Classifying Dementia Level

Dementia Severity Index: A Threshold-Based Approach to Classifying Dementia Level

Assigning channel weights using an attention mechanism: an EEG interpolation algorithm

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