- Principles of Neural Coding from EEG Signals

Decoding Color Visual Working Memory from EEG Signals Using Graph Convolutional Neural Networks

International Journal of Neural Systems ◽

10.1142/s0129065722500034 ◽

2021 ◽

Author(s):

Xiaowei Che ◽

Yuanjie Zheng ◽

Xin Chen ◽

Sutao Song ◽

Shouxin Li

Keyword(s):

Working Memory ◽

Visual Working Memory ◽

Neural Coding ◽

Memory Performance ◽

Eeg Signals ◽

Convolutional Network ◽

Convolutional Networks ◽

Behavioral Measurement ◽

Scalp Electroencephalogram ◽

Decoding Accuracy

Color has an important role in object recognition and visual working memory (VWM). Decoding color VWM in the human brain is helpful to understand the mechanism of visual cognitive process and evaluate memory ability. Recently, several studies showed that color could be decoded from scalp electroencephalogram (EEG) signals during the encoding stage of VWM, which process visible information with strong neural coding. Whether color could be decoded from other VWM processing stages, especially the maintaining stage which processes invisible information, is still unknown. Here, we constructed an EEG color graph convolutional network model (ECo-GCN) to decode colors during different VWM stages. Based on graph convolutional networks, ECo-GCN considers the graph structure of EEG signals and may be more efficient in color decoding. We found that (1) decoding accuracies for colors during the encoding, early, and late maintaining stages were 81.58%, 79.36%, and 77.06%, respectively, exceeding those during the pre-stimuli stage (67.34%), and (2) the decoding accuracy during maintaining stage could predict participants’ memory performance. The results suggest that EEG signals during the maintaining stage may be more sensitive than behavioral measurement to predict the VWM performance of human, and ECo-GCN provides an effective approach to explore human cognitive function.

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Plasticity of the neural coding metaphor: An unnoticed rhetoric in scientific discourse

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900133x ◽

2019 ◽

Vol 42 ◽

Author(s):

Giulia Frezza ◽

Pierluigi Zoccolotti

Keyword(s):

Discourse Analysis ◽

Neural Coding ◽

Scientific Discourse ◽

Convincing Argument ◽

Unified View ◽

Brain Behavior

Abstract The convincing argument that Brette makes for the neural coding metaphor as imposing one view of brain behavior can be further explained through discourse analysis. Instead of a unified view, we argue, the coding metaphor's plasticity, versatility, and robustness throughout time explain its success and conventionalization to the point that its rhetoric became overlooked.

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Cognition and Neural Coding: Perspectives for Audiologists

Perspectives of the ASHA Special Interest Groups ◽

10.1044/persp3.sig6.61 ◽

2018 ◽

Vol 3 (6) ◽

pp. 61-76

Author(s):

Leslie D. Grush ◽

Frederick J. Gallun ◽

Curtis J. Billings

Keyword(s):

Neural Coding

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Complexity Measures of Brain Electrophysiological Activity

Journal of Psychophysiology ◽

10.1027/0269-8803/a000024 ◽

2010 ◽

Vol 24 (2) ◽

pp. 131-135 ◽

Cited By ~ 5

Author(s):

Włodzimierz Klonowski ◽

Pawel Stepien ◽

Robert Stepien

Keyword(s):

Brain Activity ◽

Deterministic Chaos ◽

Epileptic Seizures ◽

Eeg Signal ◽

Eeg Signals ◽

Complexity Measures ◽

Electrophysiological Activity ◽

Signal Complexity ◽

Physiological Sleep ◽

The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

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NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

International Conference on Technics Technologies and Education ◽

10.15547/ictte.2019.02.095 ◽

2019 ◽

pp. 139-145

Author(s):

Selma Büyükgöze

Keyword(s):

Brain Computer Interface ◽

Computer Interface ◽

Electrode Placement ◽

Eeg Signals ◽

Non Invasive ◽

Brain Signals ◽

Brain States ◽

The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

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