Learning to control brain activity: a review of the production and control of EEG components for driving brain-computer interface (BCI) systems.

By Eleanor Curran, M. Stokes. Brain and Cognition, 2003

448 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b7ef724396bae909b0966b8bc40f6d1d16ca78c5

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In Cognitive Neuroscience

Citations in the corpus

Introduction to machine learning for brain imaging (2011, background): “…the ﬁ eld of real-time decoding for brain – computer interfacing (BCI) has been a technology motor (e.g. Dornhege et al., 2007; Kübler and Kotchoubey, 2007; Kübler and Müller, 2007; Wolpaw, 2007; Birbaumer, 2006; Pfurtscheller et al., 2005;CurranandStokes,2003;Wolpawetal.,2002;Kübleretal.,2001).”
The non-invasive Berlin Brain-Computer Interface: Fast acquisition of effective performance in untrained subjects (2007, background): “There is a huge variety of BCI systems (see Pfurtscheller et al., 2005; Wolpaw et al., 2002; Kübler et al., 2001; Dornhege et al., 2007b; Curran and Stokes, 2003).”
The BCI competition III: validating alternative approaches to actual BCI problems (2006, background): “The system estimates the intent of the human user from her/his brain signals measured at microscopic, mesoscopic, or macroscopic scale, cf. [1], [ 2 ], [3], [4] for an overview.”
Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring. (2008, unknown): “…communication that is solely based on brain signals, independent from muscles or peripheral nerves (see Wolpaw et al. (2002); Kübler et al. (2001); Curran and Stokes (2003); Kübler and Müller (2007); Dornhege et al. (2007a); Carmena et al. (2003) for a broader overview and background information).”
Motor imagery and action observation: modulation of sensorimotor brain rhythms during mental control of a brain-computer interface. (2009, background): “Learning to control brain activity for driving a BCI is a complex task and in this situation, not only physiological but also psychological factors like motivation, attention or excitement may play an important role (Curran and Stokes, 2003; Nijboer et al., 2008).”
Towards adaptive classification for BCI (2006, background): “This intent is estimated from brain signals measured via signals from the scalp or from invasive techniques, cf [6, 17, 32] for an overview.”
Combined Optimization of Spatial and Temporal Filters for Improving Brain-Computer Interfacing (2006, unknown): “[2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13].”
Optimizing the Channel Selection and Classification Accuracy in EEG-Based BCI (2011, background): “Since the l1/l2 norm is scale invariant, it gives the same sparseness for [0 0 3 5] and [0 0 6 10], but the l1 and lp norms do not give the same sparseness.”
The Berlin brain-computer interface: EEG-based communication without subject training (2006, background): “There is a huge variety of BCI systems, see [1]–[4] for a broad overview.”
Adaptive BCI based on variational Bayesian Kalman filtering: an empirical evaluation (2004, methodology): “We attribute this to a better experimental protocol [17], [ 42 ].”
Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data (2016, methodology): “…methods for time series neuroimaging data such as MEG/EEG have been extensively applied in brain – computer interfaces (BCI; Müller et al., 2008; Curran & Stokes, 2003; Wolpaw, Birbaumer, McFarland, Pfurtscheller, & Vaughan, 2002; Kübler, Kotchoubey, Kaiser, Wolpaw, & Birbaumer, 2001; Farwell…”
Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. (2005, unknown): “, Fmental video_) [4].”
Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior (2019, background): “A better understanding of the neural processes supporting BCI learning is an important first step towards the development of BCI therapies and the identification of specific individuals who are good candidates for treatment [27, 56].”