Current trends in Graz Brain-Computer Interface (BCI) research.

By Gert Pfurtscheller, Christa Neuper, Christoph Guger, W. Harkam, Herbert Ramoser, A. Schlögl, Bernhard Obermaier, M. Pregenzer. IEEE transactions on rehabilitation engineering, 2000

575 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/a0ed938083fc44e9006641b864a69e364804acb2

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In Cognitive Neuroscience

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Citations in the corpus

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].”
Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface (2007, unknown): “A detailed description of the method can be found in Rabiner (1989). We used the Hidden Markov Model Toolkit (HTK) from the Department of Engineering of Cambridge University, United Kingdom for our implementation (Young et al.”
EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges (2019, methodology): “Adaptive autoregressive (AAR) modelling [26,67,68] involves ﬁtting an adaptive model to data segments, and in the literature, model parameters have been estimated using recursive least-squares [69], least mean squares [68] and Kalman ﬁlter approaches [70].”
A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals (2007, background): “…Common spatial patterns (CSP) (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)…”
A brain–computer interface using electrocorticographic signals in humans (2004, unknown): “Sensorimotor rhythms comprise μ (8–12 Hz), β (18–26 Hz) and γ (>30 Hz) oscillations [6, 19–22].”
Brain-computer interfaces for communication and control. (2002, methodology): “Bit rate is shown both as bits/trial (i.e. bits/selection ), and as bits/min when 12 selections are made per min (a rate comparable to that of several current BCIs (e.g. Birbaumer et al., 2000; Donchin et al., 2000; Pfurtscheller et al., 2000a; Wolpaw et al., 2000b )).”
Brain-computer communication: unlocking the locked in. (2001, methodology): “This difference in the EEG frequency patterns over both hemispheres was used to control a cursor on a computer screen in one dimension (Pfurtscheller et al., 2000).”
Design and implementation of a brain-computer interface with high transfer rates (2002, background): “EEG patterns associated with different mental tasks [6], [7], P300 potentials [8], [9], and visual evoked potentials (VEPs) [10], [11].”
Could Anyone Use a BCI? (2010, background): “ally by testing BCIs with healthy subjects in laboratories instead of target users in home or hospital settings (Pfurtscheller et al. 2000; Kübler et al. 2001; Wolpaw et al. 2002).”
EMG contamination of EEG: spectral and topographical characteristics. (2003, methodology): “Several present-day BCI systems rely on mu (8 – 12 Hz) or beta (18 – 24 Hz) rhythms recorded from the scalp over sensorimotor cortices (e.g. Pfurtscheller et al., 2000; Wolpaw et al., 2000; Kostov and Polak, 2000).”
Comparative Analysis of Spectral Approaches to Feature Extraction for EEG-Based Motor Imagery Classification (2008, background): “, [8], represents a different concept of feature extraction.”
Conversion of EEG activity into cursor movement by a brain-computer interface (BCI) (2004, methodology): “, [14] and [15]) or common spatial filters (e.”
A BCI-based environmental controller for the motion-disabled. (2003, background): “The command for device control can be generated by self-regulated mu rhythm [2]‐[4], motor imagery [ 5 ], [6], or visual evoked potential (VEP) [7], [8].”
Information transfer rate in a five-classes brain-computer interface (2001, methodology): “ture work should focus not only on the search of suitable mental tasks, but also on improving the classification methods [13], [5], because even a slight increase of the classification accuracy can have a great influence on the information transfer rate.”
Pay attention!: designing adaptive agents that monitor and improve user engagement (2012, background): “Although research in this area has succeeded in various ways such as allowing disabled users to control a mouse cursor, these systems often require extensive tuning and training on both the part of the system and the user and are rarely generalizable across multiple users [36, 4].”
Control of a humanoid robot by a noninvasive brain–computer interface in humans (2008, background): “…this article are in colour only in the electronic version) Advances in neuroscience and computer technology have made possible a number of recent demonstrations of direct brain control of devices such as a cursor on a computer screen [1] [2] [3] [4] [5] and various prosthetic devices [6] [7] [8].”