FUNDAMENTALS OF EEG MEASUREMENT

By M. Teplan. 2002

1,883 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2eda7ede68dac52e32065c72fbdd6ae67da36e10

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Citations in the corpus

Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review (2022, unknown): “EEG is described as alternating-type electrical activity recorded from the surface of the scalp using metal electrodes and conductive medium [42].”
Deep in thought while driving: An EEG study on drivers’ cognitive distraction (2014, background): “EEG recording is completely non-invasive and can be applied repeatedly to patients, normal adults, and children with no risk or limitation (Teplan, 2002).”
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning (2018, background): “Low-frequency signals have to be well resolved in the frequency band, as signals a few Hz apart can have dramatically different origins (e.g. Theta brain waves (4 to 8 Hz ) and Alpha brain waves (8 to 13 Hz ) [81]).”
Brain activity, underlying mood and the environment: A systematic review (2019, background): “Brain imaging is useful for measuring the effects of unconscious or masked stimuli (Teplan, 2002; Whalen et al., 1998).”
Brain Computer Interfaces, a Review (2012, background): “Multi-channel configurations can comprise up to 128 or 256 active electrodes [18].”
Deep learning for electroencephalogram (EEG) classification tasks: a review (2019, background): “Emotion recognition tasks typically involve having subjects watch video clips which have been assigned specific emotions by experts prior to the viewing [27].”
Flexible Sensors—From Materials to Applications (2019, background): “However, in particular in the context of biomonitoring, these electrodes are inconvenient and exhibit poor long term stability [537,538].”
Emotions Recognition Using EEG Signals: A Survey (2019, background): “A typical adult EEG signal, when measured from the scalp, is about 10-100 mV [18].”
Emotion Recognition based on EEG using LSTM Recurrent Neural Network (2017, background): “EEG is defined as the electrical activity of an alternating type recorded from the scalp surface after being picked up by metal electrodes and conductive media [1].”
Quantitative assessment of signal quality and usability of EEG and EMG recordings with PEDOT:PSS-coated microneedle electrodes (2025, unknown): “Thanks to significant advancements in bioelectric acquisition technology, electrocardiography (ECG), electromyography (EMG), and electroencephalography (EEG) have become essential tools in both clinical and neuroscience fields (Grosse et al., 2002; Teplan, 2002; Anbalagan et al., 2023).”