Motor imagery and direct brain-computer communication

By Gert Pfurtscheller, Christa Neuper. Proceedings of the IEEE, 2001

2,015 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/fa6ce46ea28da648bcb41eea322c905dd85d692d

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces (2015, background): “As an example, during left hand motor imagery (i.e., the imagination of a left hand movement), the EEG band power in the (cid:2) (8–12 Hz) and (16–24 Hz) frequency bands will decrease in the right sensorimotor cortex [30].”
Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016 (2020, neutral): “1) Motor imagery (MI) [27], which can modify neuronal activity in primary sensorimotor areas, is similar to a real executed movement.”
On the suitability of near-infrared (NIR) systems for next-generation brain-computer interfaces. (2004, background): “Keywords: brain–computer interface, near-infrared technology, functional brain imaging, motor imagery (Some figures in this article are in colour only in the electronic version) 0967-3334/04/040815+08$30.00 © 2004 IOP Publishing Ltd Printed in the UK 815”
Subject-Independent Brain–Computer Interfaces Based on Deep Convolutional Neural Networks (2019, methodology): “Over the course of numerous BCI studies [16]–[21], growing attention has been dedicated to the analysis of electroencephalography (EEG) signals, especially by movement imagination, called motor imagery (MI) [1], [2], [8], [12], [14], [16], [22].”
Self-Paced (Asynchronous) BCI Control of a Wheelchair in Virtual Environments: A Case Study with a Tetraplegic (2007, methodology): “BCI technology deals with the development of a direct communication channel between the human brain and machines which does not require any motor activity [11, 12].”
Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms (2011, methodology): “erally measured by ElectroEncephaloGraphy (EEG) [1].”
Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface project (2003, background): “Most BCIs are based on synchronous protocols where the subject must follow a fixed repetitive scheme to switch from one mental task to the next [7], [9], [10].”
A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals (2007, methodology): “…(Blanchard and Blankertz 2004, Dornhege et al 2003, 2004, Guger et al 2000b, Krauledat et al 2004, Muller et al 2003b, Pfurtscheller et al 2000, Pfurtscheller and Neuper 2001, Ramoser et al 2000, Townsend et al 2004, Xu et al 2004b) Principal component analysis (PCA) (Chapin et al 1999, Guan…”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “noninvasive BMIs, including a multilayer perceptron (22, 161, 369, 428, 431, 691), adaptive logic network (444, 445), tree-based neural network (380), and learning-vectorquantization (402, 460, 632).”
Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. (2005, background): “, small intra-subject variability), there are also conflicting resultsof aportionofparticipants,whodonot show the expected imagery-related EEG changes [22].”
The Hybrid BCI (2010, background): “Hence, motor imagery produces changes not only in characteristic EEG patterns (Pfurtscheller and Neuper, 2001), but also in the HR (Pfurtscheller et al., 2006, 2008b).”
Motor imagery and action observation: modulation of sensorimotor brain rhythms during mental control of a brain-computer interface. (2009, methodology): “Examples of feedback sequences for realistic (A) and abstract (B) feedback type in misclassified trial (A.2) are displayed. protocol (Neuper et al., 1999; Pfurtscheller and Neuper, 2001).”
Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition (2020, neutral): “Common spatial patterns (CSPs) and its variations are widely used EEG spatial filtering algorithms [22]–[24].”
EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces (2016, methodology): “In contrast to ERPbased BCIs, which rely mainly on the detection of the ERP waveform from some external event or stimulus, oscillatory BCIs use the signal power of specific EEG frequency bands for external control and are generally asynchronous [47].”