Prediction of arm movement trajectories from ECoG-recordings in humans.

By T. Pistohl, Tonio Ball, A. Schulze-Bonhage, Ad Aertsen, Carsten Mehring. 2008

375 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience, Cognitive Neuroscience

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Mikhail A. Lebedev
2. Miguel A. L. Nicolelis
3. Gert Pfurtscheller
4. Niels Birbaumer
5. Christa Neuper
6. Carsten Mehring
7. Francis R. Bach
8. K. Müller

In Cellular and Molecular Neuroscience

1. Jonathan R. Wolpaw
2. Benjamin Blankertz
3. Niels Birbaumer
4. Gert Pfurtscheller
5. K. Müller
6. Mikhail A. Lebedev
7. Miguel A. L. Nicolelis
8. Christa Neuper

In Cognitive Neuroscience

1. Benjamin Blankertz
2. Jonathan R. Wolpaw
3. K. Müller
4. Niels Birbaumer
5. Gert Pfurtscheller
6. Fabien Lotte
7. Cuntai Guan
8. Mikhail A. Lebedev

Citations in the corpus

• Filtering with State-Observation Examples via Kernel Monte Carlo Filter (2013, methodology): “is et al., 2002; Wolf et al., 2005; Ferris et al., 2006). In brain computer interface applications, states are defined as positions of a device to be manipulated, while observations are brain signals (Pistohl et al., 2008; Wang et al., 2011). In these applications, it is hard to define the observation”
• Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects (2009, background): “Despite these obstacles, impressive results have emerged from short-term ECoG studies, focusing mainly on the capacities of ECoG signals for use in a human BCI [16,20, 34,35,38-48].”
• Multiple Operator-valued Kernel Learning (2012, background): “More precisely, we are interested in finger movement prediction from electrocorticographic signals [23].”
• Signal quality of simultaneously recorded invasive and non-invasive EEG (2009, background): “Moreover, there are increasing numbers of studies using ECoG as a control signal for prototypes of real-time biomedical applications such as brain– machine interfaces (BMIs) (Ball et al., 2009; Leuthardt et al., 2004; Mehring et al., 2004; Pistohl et al., 2008; Schalk et al., 2007).”
• Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges (2010, background): “Note, however, that most of the principles we put forward here can also be applied to other types of BCI, either invasive (single-unit activity [25, 70], electrocorticogram [103, 152]) or non-invasive (MEG [82, 112], fMRI [201, 211], NIRS [30, 178]).”
• Review of the BCI Competition IV (2012, background): “It is not clear which decoding accuracies could have been achieved with high performance for decoding of neural population signals: LFP (Mehring et al., 2003; Rickert et al., 2005), ECoG (Schalk et al., 2007; Pistohl et al., 2008; Ball et al., 2009), EEG (Waldert et al., 2008; Bradberry et al., 2010”
• Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “Stimulation applied through ECoG grids has been shown to evoke somatosensory sensations, as well (589, 638).”
• Macroscopic EEG reveals discriminative low-frequency oscillations in plan-to-grasp visuomotor tasks (2025, unknown): “In humans, the homologous AIP sulcus (aIPS) and ventral premotor cortex (PMv) are similarly engaged during action observation, object perception, and delayed grasp planning.”