Categorizing objects from MEG signals using EEGNet

Shi, Ran; Zhao, Yanyu; Cao, Zhiyuan; Liu, Chunyu; Kang, Yi; Zhang, Jiacai

doi:10.1007/s11571-021-09717-7

Cited by 6 publications

(2 citation statements)

References 62 publications

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“…Furthermore, the interest in CNNs has recently extended also to other recording modalities as well, e.g. the CNNs proposed to process EEG in humans have been modified and adapted to decode magnetoencephalography [56] and electrocorticography [57]. Conversely, decoding of invasive neural recordings from NHPs, particularly motor decoding, widely relies on RNNs, based on long-short-term memories (LSTMs) or gatedrecurrent units (GRUs), and on FCNNs, achieving significant performance improvements over other machine learning approaches, such as XGBoost, support vector machine (SVM), Kalman and Wiener filter, and Naïve Bayes (NB) classifiers [45,46].…”

Section: Introductionmentioning

confidence: 99%

Motor decoding from the posterior parietal cortex using deep neural networks

et al. 2023

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Objective. Motor decoding is crucial to translate the neural activity for Brain-Computer Interfaces (BCIs) and provides information on how motor states are encoded in the brain. Deep neural networks (DNNs) are emerging as promising neural decoders. Nevertheless, it is still unclear how different DNNs perform in different motor decoding problems and scenarios, and which network could be a good candidate for invasive BCIs. Approach. Fully-connected, convolutional, and recurrent neural networks (FCNNs, CNNs, RNNs) were designed and applied to decode motor states from neurons recorded from V6A area in the posterior parietal cortex (PPC) of macaques. Three motor tasks were considered, involving reaching and reach-to-grasping (the latter under two illumination conditions). DNNs decoded 9 reaching endpoints in 3-D space or 5 grip types using a sliding window approach within the trial course. To evaluate decoders simulating a broad variety of scenarios, the performance was also analyzed while artificially reducing the number of recorded neurons and trials, and while performing transfer learning from one task to another. Finally, the accuracy time course was used to analyze V6A motor encoding. Main results. DNNs outperformed a classic Naïve Bayes classifier, and CNNs additionally outperformed XGBoost and Support Vector Machine classifiers across the motor decoding problems. CNNs resulted the top-performing DNNs when using less neurons and trials, and task-to-task transfer learning improved performance especially in the low data regime. Lastly, V6A neurons encoded reaching and reach-to-grasping properties even from action planning, with the encoding of grip properties occurring later, closer to movement execution, and appearing weaker in darkness. Significance. Results suggest that CNNs are effective candidates to realize neural decoders for invasive BCIs in humans from PPC recordings also reducing BCI calibration times (transfer learning), and that a CNN-based data-driven analysis may provide insights about the encoding properties and the functional roles of brain regions.

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Section: Introductionmentioning

confidence: 99%

Motor decoding from the posterior parietal cortex using deep neural networks

et al. 2023

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“…EEGNet is a compacted convolutional neural network incorporating depthwise and separable convolutions, enabling the effective capture of both spatial and temporal information in EEG signals. Several studies have confirmed the effectiveness of EEGNet in analyzing object-related ERP ( [19], [20], [21], [22], [23]) and other EEG features such as P300 [24]. Nonetheless, comprehending the effectiveness of the EEGNet model requires an explanation of how the model learned from EEG data.…”

Section: Introductionmentioning

confidence: 99%

Ventral and Dorsal Stream EEG Channels: Key Features for EEG-Based Object Recognition and Identification

Leong,

Do,

Lin

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Object recognition and object identification are multifaceted cognitive operations that require various brain regions to synthesize and process information. Prior research has evidenced the activity of both visual and temporal cortices during these tasks. Notwithstanding their similarities, object recognition and identification are recognized as separate brain functions. Drawing from the two-stream hypothesis, our investigation aims to understand whether the channels within the ventral and dorsal streams contain pertinent information for effective model learning regarding object recognition and identification tasks. By utilizing the data we collected during the object recognition and identification experiment, we scrutinized EEGNet models, trained using channels that replicate the two-stream hypothesis pathways, against a model trained using all available channels. The outcomes reveal that the model trained solely using the temporal region delivered a high accuracy level in classifying four distinct object categories. Specifically, the object recognition and object identification models achieved an accuracy of 89% and 85%, respectively. By incorporating the channels that mimic the ventral stream, the model's accuracy was further improved, with the object recognition model and object identification model achieving an accuracy of 95% and 94%, respectively. Furthermore, the Grad-CAM result of the trained models revealed a significant contribution from the ventral and dorsal stream channels toward the training of the EEGNet model. The aim of our study is to pinpoint the optimal channel configuration that provides a swift and accurate brain-computer interface system for object recognition and identification.

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