Feasibility of decoding visual information from EEG

Wilson, Holly; Chen, Xi; Golbabaee, Mohammad; Proulx, Michael J.; O’Neill, Eamonn

doi:10.1080/2326263x.2023.2287719

Cited by 3 publications

(2 citation statements)

References 144 publications

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“…They also argued that the rapid-event design seemed to weaken the image-related neural responses due to the possible cognitive load and fatigue effect compared to the block design. Some researchers [15], [16], [17], [18] pointed out that block design is essential because humans tend to react more consistently and respond faster when conditions are presented in blocks [19], [20]. Wilson et al [18] advised that classification work that decodes from block design datasets is the most suitable approach until advances are made to reduce noise.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…Some researchers [15], [16], [17], [18] pointed out that block design is essential because humans tend to react more consistently and respond faster when conditions are presented in blocks [19], [20]. Wilson et al [18] advised that classification work that decodes from block design datasets is the most suitable approach until advances are made to reduce noise.…”

Section: Introduction and Related Workmentioning

confidence: 99%

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Decoding Different Reach-and-Grasp Movements Using Noninvasive Electroencephalogram

Zhang

Wang

et al. 2021

Front. Neurosci.

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Grasping is one of the most indispensable functions of humans. Decoding reach-and-grasp actions from electroencephalograms (EEGs) is of great significance for the realization of intuitive and natural neuroprosthesis control, and the recovery or reconstruction of hand functions of patients with motor disorders. In this paper, we investigated decoding five different reach-and-grasp movements closely related to daily life using movement-related cortical potentials (MRCPs). In the experiment, nine healthy subjects were asked to naturally execute five different reach-and-grasp movements on the designed experimental platform, namely palmar, pinch, push, twist, and plug grasp. A total of 480 trials per subject (80 trials per condition) were recorded. The MRCPs amplitude from low-frequency (0.3–3 Hz) EEG signals were used as decoding features for further offline analysis. Average binary classification accuracy for grasping vs. the no-movement condition peaked at 75.06 ± 6.8%. Peak average accuracy for grasping vs. grasping conditions of 64.95 ± 7.4% could be reached. Grand average peak accuracy of multiclassification for five grasping conditions reached 36.7 ± 6.8% at 1.45 s after the movement onset. The analysis of MRCPs indicated that all the grasping conditions are more pronounced than the no-movement condition, and there are also significant differences between the grasping conditions. These findings clearly proved the feasibility of decoding multiple reach-and-grasp actions from noninvasive EEG signals. This work is significant for the natural and intuitive BCI application, particularly for neuroprosthesis control or developing an active human–machine interaction system, such as rehabilitation robot.

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Section: Introduction and Related Workmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

Decoding Different Reach-and-Grasp Movements Using Noninvasive Electroencephalogram

Zhang

Wang

et al. 2021

Front. Neurosci.

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Image classification and reconstruction from low-density EEG

Guenther,

Kosmyna,

Maes

2024

Sci Rep

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Recent advances in visual decoding have enabled the classification and reconstruction of perceived images from the brain. However, previous approaches have predominantly relied on stationary, costly equipment like fMRI or high-density EEG, limiting the real-world availability and applicability of such projects. Additionally, several EEG-based paradigms have utilized artifactual, rather than stimulus-related information yielding flawed classification and reconstruction results. Our goal was to reduce the cost of the decoding paradigm, while increasing its flexibility. Therefore, we investigated whether the classification of an image category and the reconstruction of the image itself is possible from the visually evoked brain activity measured by a portable, 8-channel EEG. To compensate for the low electrode count and to avoid flawed predictions, we designed a theory-guided EEG setup and created a new experiment to obtain a dataset from 9 subjects. We compared five contemporary classification models with our setup reaching an average accuracy of 34.4% for 20 image classes on hold-out test recordings. For the reconstruction, the top-performing model was used as an EEG-encoder which was combined with a pretrained latent diffusion model via double-conditioning. After fine-tuning, we reconstructed images from the test set with a 1000 trial 50-class top-1 accuracy of 35.3%. While not reaching the same performance as MRI-based paradigms on unseen stimuli, our approach greatly improved the affordability and mobility of the visual decoding technology.

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