Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects

Hill, N. Jeremy; Lal, TN; Schröder, Michael; Hinterberger, Thilo; Wilhelm, Barbara; Nijboer, Femke; Mochty, Ursula; Widman, Guido; Elger, Christian E.; Schölkopf, B; Kübler, Andrea; Birbaumer, Niels

doi:10.1109/tnsre.2006.875548

Cited by 112 publications

(78 citation statements)

References 7 publications

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“…The size of the device can also be controlled by increasing or decreasing the length, diameter, and a number of the wires which could be applied to investigate the puzzling concept of physical structures affecting the quality of LFPs in electrodes arrays [41][42][43]. Without the need for cleanroom facilities, this device can be fabricated and modified to fit and possibly improve on different applications, such as BCI systems [44,45]. Since the electrode is handcrafted, users can design ECoGs with higher electrode density without any further complication and microfabrication limitation.…”

Section: Discussionmentioning

confidence: 99%

Handcrafted Electrocorticography Electrodes for a Rodent Behavioral Model

et al. 2016

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Electrocorticography (ECoG) is a minimally invasive neural recording method that has been extensively used for neuroscience applications. It has proven to have the potential to ease the establishment of proper links for neural interfaces that can offer disabled patients an alternative solution for their lost sensory and motor functions through the use of brain-computer interface (BCI) technology. Although many neural recording methods exist, ECoG provides a combination of stability, high spatial and temporal resolution with chronic and mobile capabilities that could make BCI systems accessible for daily applications. However, many ECoG electrodes require MEMS fabricating techniques which are accompanied by various expenses that are obstacles for research projects. For this reason, this paper presents an animal study using a low cost and simple handcrafted ECoG electrode that is made of commercially accessible materials. The study is performed on a Lewis rat implanted with a handcrafted 32-channel non-penetrative ECoG electrode covering an area of 3 × 3 mm 2 on the cortical surface. The ECoG electrodes were placed on the motor and somatosensory cortex to record the signal patterns while the animal was active on a treadmill. Using a Tucker-Davis Technologies acquisition system and the software Synapse to monitor and analyze the electrophysiological signals, the electrodes obtained signals within the amplitude range of 200 µV for local field potentials with reliable spatiotemporal profiles. It was also confirmed that the handcrafted ECoG electrode has the stability and chronic features found in other commercial electrodes.

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Section: Discussionmentioning

confidence: 99%

Handcrafted Electrocorticography Electrodes for a Rodent Behavioral Model

et al. 2016

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“…Experience suggests that the quality of the signal is better for ECoG than the scalp EEG and is less contaminated with artifacts, but that the ease of recording of the scalp EEG is superior to ECoG for BCI purposes (Schalk and Leuthardt, 2011). It has also been observed in some studies that the performance of the classifier is dependent on the user population (Hill et al, 2006). Jackson, Mcclendon et al 2010;Power, Falk et al 2010;Sorger, Reithler et al 2012).…”

Section: Intracranial Recordings Ecog (Electrocorticogram)mentioning

confidence: 99%

Translational Algorithms: The Heart of a Brain Computer Interface

Singh

Daly

2014

Brain-Computer Interfaces

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Brain computer Interface (BCI) development encapsulates three basic processes: data acquisition, data processing, and device control. Since the start of the millennium the BCI development cycle has undergone a metamorphosis. This is mainly due to the increased popularity of BCI applications in both commercial and research circles. One of the focuses of BCI research is to bridge the gap between laboratory research and commercial applications using this technology.A vast variety of new approaches are being employed for BCI development ranging from novel paradigms, such as simultaneous acquisitions, through to asynchronous BCI control. The strategic usage of computational techniques, comprising the heart of the BCI system, underwrites this vast range of approaches. This chapter discusses these computational strategies and translational techniques including dimensionality reduction, feature extraction, feature selection, and classification techniques.

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“…Hypotheses, why ALS patients are not able to communicate with BCIs in the completely locked-in state include: (1) difficulty in performing the task because of cognitive deficits or lack of alertness (2) inability to modulate the cortical rhythms due to degeneration or missing feedback (3) unwillingness to cooperate (Hill et al, 2005). Especially because of (1), we need to ensure that the ALS patients are in a suitable cognitive state.…”

Section: Patient-specific Stimulation 431 Alsmentioning

confidence: 99%

Bcci - A Bidirectional Cortical Communication Interface

Walter

Bensch

Brugger

et al. 2009

Proceedings of the International Joint Conference on Computational Intelligence

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Abstract:Therapeutic methods based on efferent signals from the patients' brain have been studied extensively in the field of brain-computer interfaces and applied to paralysed and stroke patients. Invasive stimulation is used as a therapeutic tool for patients with Parkinson disease, intractable chronic pain and other neurological diseases.We give a short review of currently used applications for cortical stimulation for stroke patients and braincomputer interfaces for paralyzed patients. We propose a refined approach for stroke rehabilitation as well as the extension of the use of invasive cortical stimulation to ALS patients with an experimental setup inspired by classical conditioning to facilitate the communication with brain-computer interfaces for LIS and CLIS patients. A closed-loop system is described with sophisticated methods for the identification of recording and stimulation sites, feature extraction and adaptation of stimulation algorithms to the patient in order to design a bidirectional cortical communication interface (BCCI).

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Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects

Cited by 112 publications

References 7 publications

Handcrafted Electrocorticography Electrodes for a Rodent Behavioral Model

Handcrafted Electrocorticography Electrodes for a Rodent Behavioral Model

Translational Algorithms: The Heart of a Brain Computer Interface

Bcci - A Bidirectional Cortical Communication Interface

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