A comparison of different dimensionality reduction and feature selection methods for single trial ERP detection

Lan, Tian; Erdoğmuş, Deni̇z; Black, Lois M.; Santen, Jan P. H. van

doi:10.1109/iembs.2010.5627642

Cited by 10 publications

(2 citation statements)

References 3 publications

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“…This yielded a high dimensional feature space with d = n channels × 358 coefficients per trial. To facilitate classification by LDA, this high dimensionality was reduced by PCA to ten dimensions per trial (Lan et al, 2010). We used all 10 principal components for classification since only components with the highest variance were not ensured to be informative for classification (Lugger et al, 1998).…”

Section: Methodsmentioning

confidence: 99%

Single-trial classification of gait and point movement preparation from human EEG

Velu¹,

de²

2013

Front. Neurosci.

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Neuroimaging studies provide evidence of cortical involvement immediately before and during gait and during gait-related behaviors such as stepping in place or motor imagery of gait. Here we attempt to perform single-trial classification of gait intent from another movement plan (point intent) or from standing in place. Subjects walked naturally from a starting position to a designated ending position, pointed at a designated position from the starting position, or remained standing at the starting position. The 700 ms of recorded electroencephalography (EEG) before movement onset was used for single-trial classification of trials based on action type and direction (left walk, forward walk, right walk, left point, right point, and stand) as well as action type regardless of direction (stand, walk, point). Classification using regularized LDA was performed on a principal components analysis (PCA) reduced feature space composed of coefficients from levels 1 to 9 of a discrete wavelet decomposition using the Daubechies 4 wavelet. We achieved significant classification for all conditions, with errors as low as 17% when averaged across nine subjects. LDA and PCA highly weighted frequency ranges that included movement related potentials (MRPs), with smaller contributions from frequency ranges that included mu and beta idle motor rhythms. Additionally, error patterns suggested a spatial structure to the EEG signal. Future applications of the cortical gait intent signal may include an additional dimension of control for prosthetics, preemptive corrective feedback for gait disturbances, or human computer interfaces (HCI).

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Section: Methodsmentioning

confidence: 99%

Single-trial classification of gait and point movement preparation from human EEG

Velu¹,

de²

2013

Front. Neurosci.

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“…From the works addressed above, one can infer that data-based features for setting up ML/DL models are of key importance. In this context, two main steps can be identified: (a) feature extraction, which can be done using specific layers of consolidated CNN architectures, such as VGG16-widely known for its capabilities in artificial deep vision tasks [46][47][48]-benefiting from ImageNet [49] weights; and (b) dimensionality reduction, achievable through techniques such as, for example, principle component analysis (PCA), which is also capable of retaining the relevant variance of the data to preserve its intrinsic characteristics [50][51][52].…”

Section: Related Workmentioning

confidence: 99%

Automatic Optimization of Deep Learning Training through Feature-Aware-Based Dataset Splitting

Shahrabadi,

Adão,

Peres

et al. 2024

Algorithms

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The proliferation of classification-capable artificial intelligence (AI) across a wide range of domains (e.g., agriculture, construction, etc.) has been allowed to optimize and complement several tasks, typically operationalized by humans. The computational training that allows providing such support is frequently hindered by various challenges related to datasets, including the scarcity of examples and imbalanced class distributions, which have detrimental effects on the production of accurate models. For a proper approach to these challenges, strategies smarter than the traditional brute force-based K-fold cross-validation or the naivety of hold-out are required, with the following main goals in mind: (1) carrying out one-shot, close-to-optimal data arrangements, accelerating conventional training optimization; and (2) aiming at maximizing the capacity of inference models to its fullest extent while relieving computational burden. To that end, in this paper, two image-based feature-aware dataset splitting approaches are proposed, hypothesizing a contribution towards attaining classification models that are closer to their full inference potential. Both rely on strategic image harvesting: while one of them hinges on weighted random selection out of a feature-based clusters set, the other involves a balanced picking process from a sorted list that stores data features’ distances to the centroid of a whole feature space. Comparative tests on datasets related to grapevine leaves phenotyping and bridge defects showcase promising results, highlighting a viable alternative to K-fold cross-validation and hold-out methods.

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Feature selection and dimensionality reduction: An extensive comparison in hand gesture classification by sEMG in eight channels armband approach

Mendes

Freitas

Siqueira

et al. 2020

Biomedical Signal Processing and Control

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A comparison of different dimensionality reduction and feature selection methods for single trial ERP detection

Cited by 10 publications

References 3 publications

Single-trial classification of gait and point movement preparation from human EEG

Single-trial classification of gait and point movement preparation from human EEG

Automatic Optimization of Deep Learning Training through Feature-Aware-Based Dataset Splitting

Feature selection and dimensionality reduction: An extensive comparison in hand gesture classification by sEMG in eight channels armband approach

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