User Adapted Motor-Imaginary Brain-Computer Interface by means of EEG Channel Selection Based on Estimation of Distributed Algorithms

Astigarraga, Aitzol; Arruti, Andoni; Muguerza, Javier; Santana, Roberto; Martín, Jesús Izquierdo; Sierra, Basilio

doi:10.1155/2016/1435321

Cited by 12 publications

(10 citation statements)

References 35 publications

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“…As future work, we plan to apply this approach to non-segmented data, that is, to build a self-paced BCI system [38], [39] using a sliding time window technique and analysing the influence of the overlap among the windows in the performance of the system. Moreover, we intend to use automatic channel selection techniques [40], [41] to reduce the dimensionality and to build simpler and faster classifiers without accuracy losses. Finally, we will define some feedback to facilitate the training phase for the potential users of this kind of system [42].…”

Section: Discussionmentioning

confidence: 99%

A hierarchical architecture for recognising intentionality in mental tasks on a brain-computer interface

et al. 2019

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A brain-computer interface (BCI), based on motor imagery EEG, uses information extracted from the electroencephalography signals generated by a person who intends to perform any action. One of the most important issues of current research is how to detect automatically whether the user intends to send some message to a certain device. This study presents a proposal, based on a hierarchical structured system, for recognising intentional and non-intentional mental tasks on a BCI system by applying machine learning techniques to the EEG signals. First-level clustering is performed to distinguish between intentional control (IC) and non-intentional control (NC) state patterns. Then, the patterns recognised as IC are passed on to a second stage where supervised learning techniques are used to classify them. In BCI applications, it is critical to correctly classify NC states with a low false positive rate (FPR) to avoid undesirable effects. According to the literature, we selected a maximum FPR of 10%. Under these conditions, our proposal achieved an average test accuracy of 66.6%, with an 8.2% FPR, for the BCI competition IIIa dataset. The main contribution of this paper is the hierarchical approach, based on machine learning paradigms, which performs intentional and non-intentional discrimination and, depending on the case, classifies the intended command selected by the user.

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Section: Discussionmentioning

confidence: 99%

A hierarchical architecture for recognising intentionality in mental tasks on a brain-computer interface

et al. 2019

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“…The CSP technique was first proposed under the name Fukunaga-Koontz Transform in Fukunaga and Koontz [1970] as an extension of PCA and Müller-Gerking et al [1999] used it to discriminate electroencephalography data (EEG) in a movement task. Since then, it is a widely used technique to analyze EEG data and develop Brain Computer Interfaces (BCI), with different variations and extensions [Blankertz et al, 2007a,b, Grosse-Wentrup and Buss, 2008, Lotte and Guan, 2011, Wang et al, 2012, Astigarraga et al, 2016, Darvish Ghanbar et al, 2021. Samek et al [2014] offer a divergence-based framework including several extentions of CSP.…”

Section: Introductionmentioning

confidence: 99%

dbcsp: User-friendly R package for Distance-Based Common Spacial Patterns

Rodriguez,

Irigoien,

Sierra

et al. 2021

Preprint

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Common Spacial Patterns (CSP) is a widely used method to analyse electroencephalography (EEG) data, concerning the supervised classification of brain's activity. More generally, it can be useful to distinguish between multivariate signals recorded during a time span for two different classes. CSP is based on the simultaneous diagonalization of the average covariance matrices of signals from both classes and it allows to project the data into a low-dimensional subspace. Once data are represented in a low-dimensional subspace, a classification step must be carried out. The original CSP method is based on the Euclidean distance between signals and here, we extend it so that it can be applied on any appropriate distance for data at hand. Both, the classical CSP and the new Distance-Based CSP (DB-CSP) are implemented in an R package, called dbcsp.

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“…In this paper, a new approach for video action recognition is presented. The Common Spatial Pattern algorithm is used, a method normally applied in Brain Computer Interface (BCI) for EEG systems [11]. Videos are recorded and processed with OpenPose [12] software in order to obtain a sequence of skeleton data.…”

Section: Introductionmentioning

confidence: 99%

Shedding Light on People Action Recognition in Social Robotics by Means of Common Spatial Patterns

Rodríguez-Moreno

Martínez-Otzeta

Goienetxea

et al. 2020

Sensors

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Action recognition in robotics is a research field that has gained momentum in recent years. In this work, a video activity recognition method is presented, which has the ultimate goal of endowing a robot with action recognition capabilities for a more natural social interaction. The application of Common Spatial Patterns (CSP), a signal processing approach widely used in electroencephalography (EEG), is presented in a novel manner to be used in activity recognition in videos taken by a humanoid robot. A sequence of skeleton data is considered as a multidimensional signal and filtered according to the CSP algorithm. Then, characteristics extracted from these filtered data are used as features for a classifier. A database with 46 individuals performing six different actions has been created to test the proposed method. The CSP-based method along with a Linear Discriminant Analysis (LDA) classifier has been compared to a Long Short-Term Memory (LSTM) neural network, showing that the former obtains similar or better results than the latter, while being simpler.

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User Adapted Motor-Imaginary Brain-Computer Interface by means of EEG Channel Selection Based on Estimation of Distributed Algorithms

Cited by 12 publications

References 35 publications

A hierarchical architecture for recognising intentionality in mental tasks on a brain-computer interface

A hierarchical architecture for recognising intentionality in mental tasks on a brain-computer interface

dbcsp: User-friendly R package for Distance-Based Common Spacial Patterns

Shedding Light on People Action Recognition in Social Robotics by Means of Common Spatial Patterns

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