Classification and Transfer Learning of EEG during a Kinesthetic Motor Imagery Task using Deep Convolutional Neural Networks

Craik, Alexander; Kilicarslan, Atilla; Contreras-Vidal, José L.

doi:10.1109/embc.2019.8857575

Cited by 10 publications

(4 citation statements)

References 18 publications

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“…The first design consideration is the number of convolutional layers, together with the type of end classifier. In MI tasks, 70% of CNN models use a rectified linear unit (ReLU) as the layer's activation function, while the vast majority of classifier fully connected layers employ a softmax activation function [49]. The proposed network relies on the Wide&Deep architecture for handling multiple inputs to learn deep patterns under simple rules.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

CNN-based framework using spatial dropping for enhanced interpretation of neural activity in motor imagery classification

et al. 2020

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Interpretation of brain activity responses using motor imagery (MI) paradigms is vital for medical diagnosis and monitoring. Assessed by machine learning techniques, identification of imagined actions is hindered by substantial intra-and inter-subject variability. Here, we develop an architecture of Convolutional Neural Networks (CNN) with an enhanced interpretation of the spatial brain neural patterns that mainly contribute to the classification of MI tasks. Two methods of 2D-feature extraction from EEG data are contrasted: Power Spectral Density and Continuous Wavelet Transform. For preserving the spatial interpretation of extracting EEG patterns, we project the multi-channel data using a topographic interpolation. Besides, we include a spatial dropping algorithm to remove the learned weights that reflect the localities not engaged with the elicited brain response. We evaluate two labeled scenarios of MI tasks: bi-class and three-class. Obtained results in an MI database show that the thresholding strategy combined with Continuous Wavelet Transform improves the accuracy and enhances the interpretability of CNN architecture, showing that the highest contribution clusters over the sensorimotor cortex with a differentiated behavior of rhythms µ and β.

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Section: Discussion and Concluding Remarksmentioning

confidence: 99%

CNN-based framework using spatial dropping for enhanced interpretation of neural activity in motor imagery classification

et al. 2020

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“…The first design consideration is the number of convolutional layers, together with the type of end classifier. In MI tasks, 70% of CNN models use a rectified linear unit (ReLU) as the layers activation function, while the vast majority of classifier fully-connected layers employ a softmax activation function [46]. The proposed network is fully-connected that relies on the Wide&Deep architecture to handle multiple inputs to learn deep patterns under simple rules.…”

Section: A02tmentioning

confidence: 99%

CNN-based Framework using Spatial Dropping for Enhanced Interpretation of Neural Activity in Motor Imagery Classification

Collazos-Huertas

Meza

Domínguez

2020

Preprint

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Interpretation of brain activity responses using Motor Imagery (MI) paradigms is vital for medical diagnosis and monitoring. Assessed by machine learning techniques, identification of imagined actions is hindered by substantial intra and inter subject variability. Here, we develop an architecture of Convolutional Neural Networks (CNN) with enhanced interpretation of the spatial brain neural patterns that mainly contribute to the classification of MI tasks. Two methods of 2D-feature extraction from EEG data are contrasted: Power Spectral Density and Continuous Wavelet Transform. For preserving the spatial interpretation of extracting EEG patterns, we project the multi-channel data using a topographic interpolation. Besides, we include a spatial dropping algorithm to remove the learned weights that reflect the localities not engaged with the elicited brain response. Obtained results in a bi-task MI database show that the thresholding strategy in combination with Continuous Wavelet Transform improves the accuracy and enhances the interpretability of CNN architecture, showing that the highest contribution clusters over the sensorimotor cortex with differentiated behavior between μ and β rhythms.

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“…While EEG measurements are affected by many factors, including physiological and non-physiological artifacts [3] resulting in low signal-to-noise ratios [4], recent advancements in de-noising (e.g., [5,6]) and deep learning [7,8] techniques have driven the emergence of viable clinical and non-clinical BCI applications based on scalp EEG [1,2,9]. These applications include, but are not limited to, seizure state prediction [10,11], sleep stage analysis [12], cognitive workload assessment [13], motor-imagery-based brain-computer interface (BCI) systems [14,15], neurorehabilitation [16,17], multi-modal and multi-brain-computer interfaces [18], braincontrolled vehicles [19], EEG-based home control [20], virtual reality [21], and interactive virtual environments [22]. While the future of these proof-of-concept BCI-enabled applications is promising, there are a number of technical challenges that remain before the widespread translation and adoption of these systems is realized.…”

Section: Introductionmentioning

confidence: 99%

Design and Validation of a Low-Cost Mobile EEG-Based Brain–Computer Interface

Craik,

González-España,

Alamir

et al. 2023

Sensors

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Objective: We designed and validated a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain–computer (BCI) interface and internet-of-things (IoT) applications. Approach: The EEG-based BCI headset was designed from commercial off-the-shelf (COTS) components using a multi-pronged approach that balanced interoperability, cost, portability, usability, form factor, reliability, and closed-loop operation. Main Results: The adjustable headset was designed to accommodate 90% of the population. A patent-pending self-positioning dry electrode bracket allowed for vertical self-positioning while parting the user’s hair to ensure contact of the electrode with the scalp. In the current prototype, five EEG electrodes were incorporated in the electrode bracket spanning the sensorimotor cortices bilaterally, and three skin sensors were included to measure eye movement and blinks. An inertial measurement unit (IMU) provides monitoring of head movements. The EEG amplifier operates with 24-bit resolution up to 500 Hz sampling frequency and can communicate with other devices using 802.11 b/g/n WiFi. It has high signal–to–noise ratio (SNR) and common–mode rejection ratio (CMRR) (121 dB and 110 dB, respectively) and low input noise. In closed-loop BCI mode, the system can operate at 40 Hz, including real-time adaptive noise cancellation and 512 MB of processor memory. It supports LabVIEW as a backend coding language and JavaScript (JS), Cascading Style Sheets (CSS), and HyperText Markup Language (HTML) as front-end coding languages and includes training and optimization of support vector machine (SVM) neural classifiers. Extensive bench testing supports the technical specifications and human-subject pilot testing of a closed-loop BCI application to support upper-limb rehabilitation and provides proof-of-concept validation for the device’s use at both the clinic and at home. Significance: The usability, interoperability, portability, reliability, and programmability of the proposed wireless closed-loop BCI system provides a low-cost solution for BCI and neurorehabilitation research and IoT applications.

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Classification and Transfer Learning of EEG during a Kinesthetic Motor Imagery Task using Deep Convolutional Neural Networks

Cited by 10 publications

References 18 publications

CNN-based framework using spatial dropping for enhanced interpretation of neural activity in motor imagery classification

CNN-based framework using spatial dropping for enhanced interpretation of neural activity in motor imagery classification

CNN-based Framework using Spatial Dropping for Enhanced Interpretation of Neural Activity in Motor Imagery Classification

Design and Validation of a Low-Cost Mobile EEG-Based Brain–Computer Interface

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