Error-related Potential Variability: Exploring the Effects on
Classification and Transferability
- URL: http://arxiv.org/abs/2301.06555v1
- Date: Mon, 16 Jan 2023 18:39:18 GMT
- Title: Error-related Potential Variability: Exploring the Effects on
Classification and Transferability
- Authors: Benjamin Poole and Minwoo Lee
- Abstract summary: Error-related potentials (ErrPs) are a particular brain signal elicited when one commits or observes an erroneous event.
ErrPs vary from instance to instance as they are combined with an assortment of other brain signals, biological noise, and external noise.
Recent works have revealed particular cognitive processes such as awareness, embodiment, and predictability that contribute to ErrP variations.
- Score: 3.6930948691311016
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Brain-Computer Interfaces (BCI) have allowed for direct communication from
the brain to external applications for the automatic detection of cognitive
processes such as error recognition. Error-related potentials (ErrPs) are a
particular brain signal elicited when one commits or observes an erroneous
event. However, due to the noisy properties of the brain and recording devices,
ErrPs vary from instance to instance as they are combined with an assortment of
other brain signals, biological noise, and external noise, making the
classification of ErrPs a non-trivial problem. Recent works have revealed
particular cognitive processes such as awareness, embodiment, and
predictability that contribute to ErrP variations. In this paper, we explore
the performance of classifier transferability when trained on different ErrP
variation datasets generated by varying the levels of awareness and embodiment
for a given task. In particular, we look at transference between observational
and interactive ErrP categories when elicited by similar and differing tasks.
Our empirical results provide an exploratory analysis into the ErrP
transferability problem from a data perspective.
Related papers
- Nonlinearity, Feedback and Uniform Consistency in Causal Structural
Learning [0.8158530638728501]
Causal Discovery aims to find automated search methods for learning causal structures from observational data.
This thesis focuses on two questions in causal discovery: (i) providing an alternative definition of k-Triangle Faithfulness that (i) is weaker than strong faithfulness when applied to the Gaussian family of distributions, and (ii) under the assumption that the modified version of Strong Faithfulness holds.
arXiv Detail & Related papers (2023-08-15T01:23:42Z) - Evaluating the structure of cognitive tasks with transfer learning [67.22168759751541]
This study investigates the transferability of deep learning representations between different EEG decoding tasks.
We conduct extensive experiments using state-of-the-art decoding models on two recently released EEG datasets.
arXiv Detail & Related papers (2023-07-28T14:51:09Z) - BISCUIT: Causal Representation Learning from Binary Interactions [36.358968799947924]
BISCUIT is a method for simultaneously learning causal variables and their corresponding binary interaction variables.
On three robotic-inspired datasets, BISCUIT accurately identifies causal variables and can even be scaled to complex, realistic environments for embodied AI.
arXiv Detail & Related papers (2023-06-16T06:10:55Z) - A Causal Framework for Decomposing Spurious Variations [68.12191782657437]
We develop tools for decomposing spurious variations in Markovian and Semi-Markovian models.
We prove the first results that allow a non-parametric decomposition of spurious effects.
The described approach has several applications, ranging from explainable and fair AI to questions in epidemiology and medicine.
arXiv Detail & Related papers (2023-06-08T09:40:28Z) - Variational $f$-Divergence and Derangements for Discriminative Mutual
Information Estimation [4.114444605090134]
We propose a novel class of discriminative mutual information estimators based on the variational representation of the $f$-divergence.
Experiments on reference scenarios demonstrate that our approach outperforms state-of-the-art neural estimators both in terms of accuracy and complexity.
arXiv Detail & Related papers (2023-05-31T16:54:25Z) - I am Only Happy When There is Light: The Impact of Environmental Changes
on Affective Facial Expressions Recognition [65.69256728493015]
We study the impact of different image conditions on the recognition of arousal from human facial expressions.
Our results show how the interpretation of human affective states can differ greatly in either the positive or negative direction.
arXiv Detail & Related papers (2022-10-28T16:28:26Z) - Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects
Estimation [103.55894890759376]
This paper introduces several building blocks that use representation learning to handle the heterogeneous feature spaces.
We show how these building blocks can be used to recover transfer learning equivalents of the standard CATE learners.
arXiv Detail & Related papers (2022-10-08T16:41:02Z) - The world seems different in a social context: a neural network analysis
of human experimental data [57.729312306803955]
We show that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals.
An analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions.
arXiv Detail & Related papers (2022-03-03T17:19:12Z) - Towards Unbiased Visual Emotion Recognition via Causal Intervention [63.74095927462]
We propose a novel Emotion Recognition Network (IERN) to alleviate the negative effects brought by the dataset bias.
A series of designed tests validate the effectiveness of IERN, and experiments on three emotion benchmarks demonstrate that IERN outperforms other state-of-the-art approaches.
arXiv Detail & Related papers (2021-07-26T10:40:59Z) - Disentangled Adversarial Transfer Learning for Physiological Biosignals [24.02384472840036]
We propose an adversarial inference approach for transfer learning to extract disentangled nuisance-robust representations from physiological biosignal data.
Results on cross-subjects transfer evaluations demonstrate the benefits of the proposed adversarial framework.
arXiv Detail & Related papers (2020-04-15T01:56:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.