Finding the Optimal Time Window for Increased Classification Accuracy during Motor Imagery

Blanco-Mora, Diego Andrés; Aldridge, Audrey; Vieira, Carolina Jorge; Vourvopoulos, Athanasios; Figueiredo, Patrícia; Badia, Sergi Bermúdez i

doi:10.5220/0010316101440151

Cited by 5 publications

(3 citation statements)

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“…EEG data processing was based on the pipeline described in [3] and included the use of temporal and spatial filtering for training a linear classifier in the OpenVibe platform [29]. Specifically, raw EEG [30].…”

Section: Data Processingmentioning

confidence: 99%

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Impact of age, VR, immersion, and spatial resolution on classifier performance for a MI-based BCI

Blanco-Mora

Aldridge

Jorge

et al. 2022

Brain-Computer Interfaces

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There are many factors outlined in the signal processing pipeline that impact brain-computer interface (BCI) performance, but some methodological factors do not depend on signal processing. Nevertheless, there is a lack of research assessing the effect of such factors. Here, we investigate the impact of VR, immersiveness, age, and spatial resolution on the classifier performance of a Motor Imagery (MI) electroencephalography (EEG)-based BCI in naïve participants. We found significantly better performance for VR compared to non-VR (15 electrodes:

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Section: Data Processingmentioning

confidence: 99%

“…There are many methodological factors that can impact BCI performance, some of which are outlined in the signal processing pipeline used in [3]. Many other factors do not depend on signal processing but can impact on the performance of such BCI systems.…”

Section: Introductionmentioning

confidence: 99%

Impact of age, VR, immersion, and spatial resolution on classifier performance for a MI-based BCI

Blanco-Mora

Aldridge

Jorge

et al. 2022

Brain-Computer Interfaces

Self Cite

View full text Add to dashboard Cite

show abstract

“…The size of the segmentation window typically covers an interval of a few seconds [33][34][35]. It is important to choose the proper window size, because there is an optimal value that can maximize the performance of the model for a given machine learning task.…”

Section: Segmentationmentioning

confidence: 99%

Overview of the EEG-Based Classification of Motor Imagery Activities Using Machine Learning Methods and Inference Acceleration with FPGA-Based Cards

Majoros

Oniga

2022

Electronics

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In this article, we provide a brief overview of the EEG-based classification of motor imagery activities using machine learning methods. We examined the effect of data segmentation and different neural network structures. By applying proper window size and using a purely convolutional neural network, we achieved 97.7% recognition accuracy on data from twenty subjects in three classes. The proposed architecture outperforms several networks used in previous research and makes the motor imagery-based BCI more efficient in some applications. In addition, we examined the performance of the neural network on a FPGA-based card and compared it with the inference speed and accuracy provided by a general-purpose processor.

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Basis and Clinical Evidence of Virtual Reality-Based Rehabilitation of Sensorimotor Impairments After Stroke

Fluet

Roy

Llorens

et al. 2022

Neurorehabilitation Technology

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Finding the Optimal Time Window for Increased Classification Accuracy during Motor Imagery

Cited by 5 publications

References 0 publications

Impact of age, VR, immersion, and spatial resolution on classifier performance for a MI-based BCI

Impact of age, VR, immersion, and spatial resolution on classifier performance for a MI-based BCI

Overview of the EEG-Based Classification of Motor Imagery Activities Using Machine Learning Methods and Inference Acceleration with FPGA-Based Cards

Basis and Clinical Evidence of Virtual Reality-Based Rehabilitation of Sensorimotor Impairments After Stroke

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