EEG Feature Extraction

Lotte, Fabien; Congedo, Marco

doi:10.1002/9781119144977.ch7

Cited by 15 publications

(7 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An online BCI system is a closed-loop, starting with the user producing a specific EEG pattern (e.g., using motor imagery) and these EEG signals being measured. Then, EEG signals are typically pre-processed using various spatial and spectral filters [23], and features are extracted from these signals in order to represent them in a compact form [140]. Finally, these EEG features are classified [141] before being translated into a command for an application [45] and before feedback is provided to users to inform them whether a specific mental command was recognized or not [170].…”

Section: Introductionmentioning

confidence: 99%

A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update

et al. 2018

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update

et al. 2018

View full text Add to dashboard Cite

show abstract

“…These sensorimotor rhythms are characterized, before and during an imagined movement, by a gradual decrease of power in—mainly—the mu-alpha (7–13 Hz) and beta (15–30 Hz) band and after the end of the motor imagery, by an increase of power in the beta band. These modulations are respectively known as Event-Related Desynchronization (ERD) and Event-Related Synchronization (ERS) or post-movement beta rebound (Pfurtscheller, 2003; Hashimoto and Ushiba, 2013; Kilavik et al, 2013; Lotte and Congedo, 2016). Two types of MI can be distinguished: Kinesthetic Motor Imageries (KMI) and Visual Motor Imageries (VMI).…”

Section: Introductionmentioning

confidence: 99%

Can a Subjective Questionnaire Be Used as Brain-Computer Interface Performance Predictor?

Rimbert

Gayraud

Bougrain

et al. 2019

Front. Hum. Neurosci.

View full text Add to dashboard Cite

Predicting a subject's ability to use a Brain Computer Interface (BCI) is one of the major issues in the BCI domain. Relevant applications of forecasting BCI performance include the ability to adapt the BCI to the needs and expectations of the user, assessing the efficiency of BCI use in stroke rehabilitation, and finally, homogenizing a research population. A limited number of recent studies have proposed the use of subjective questionnaires, such as the Motor Imagery Questionnaire Revised-Second Edition (MIQ-RS). However, further research is necessary to confirm the effectiveness of this type of subjective questionnaire as a BCI performance estimation tool. In this study we aim to answer the following questions: can the MIQ-RS be used to estimate the performance of an MI-based BCI? If not, can we identify different markers that could be used as performance estimators? To answer these questions, we recorded EEG signals from 35 healthy volunteers during BCI use. The subjects had previously completed the MIQ-RS questionnaire. We conducted an offline analysis to assess the correlation between the questionnaire scores related to Kinesthetic and Motor imagery tasks and the performances of four classification methods. Our results showed no significant correlation between BCI performance and the MIQ-RS scores. However, we reveal that BCI performance is correlated to habits and frequency of practicing manual activities.

show abstract

“…Another point to consider here is that decoding did not appeal to more elaborate, neuroengineered features (e.g., estimates of signal complexity and coherence) (Jenke, Peer, & Buss, ; Lotte & Congedo, ) or to those derived by convolutional neural networks (Schirrmeister et al, ; Sturm, Lapuschkin, Samek, & Müller, ). Admittedly, the use of more elaborate features may further boost decoding accuracy and, also, close the gap between the performance of time‐ and frequency‐based information.…”

Section: Discussionmentioning

confidence: 99%

A multivariate investigation of visual word, face, and ensemble processing: Perspectives from EEG‐based decoding and feature selection

et al. 2019

View full text Add to dashboard Cite

Dan Nemrodov and Shouyu Ling are contributed equally to this work.The University of Toronto has filed a U.S. patent application that includes portions of the method for feature selection described here. Adrian Nestor and Dan Nemrodov are co-inventors on this patent. AbstractRecent investigations have focused on the spatiotemporal dynamics of visual recognition by appealing to pattern analysis of EEG signals. While this work has established the ability to decode identity-level information (such as the identity of a face or of a word) from neural signals, much less is known about the precise nature of the signals that support such feats, their robustness across visual categories, or their consistency across human participants. Here, we address these questions through the use of EEG-based decoding and multivariate feature selection as applied to three visual categories: words, faces and face ensembles (i.e., crowds of faces). Specifically, we use recursive feature elimination to estimate the diagnosticity of time and frequencybased EEG features for identity-level decoding across three datasets targeting each of the three categories. We then relate feature diagnosticity across categories and across participants while, also, aiming to increase decoding performance and reliability. Our investigation shows that word and face processing are similar in their reliance on spatiotemporal information provided by occipitotemporal channels. In contrast, ensemble processing appears to also rely on central channels and exhibits a similar profile with word processing in the frequency domain. Further, we find that feature diagnosticity is stable across participants and is even capable of supporting cross-participant feature selection, as demonstrated by systematic boosts in decoding accuracy and feature reduction. Thus, our investigation sheds new light on the nature and the structure of the information underlying identity-level visual processing as well as on its generality across categories and participants.

show abstract

EEG Feature Extraction

Cited by 15 publications

References 23 publications

A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update

A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update

Can a Subjective Questionnaire Be Used as Brain-Computer Interface Performance Predictor?

A multivariate investigation of visual word, face, and ensemble processing: Perspectives from EEG‐based decoding and feature selection

Contact Info

Product

Resources

About