Erratum to: Electroencephalography (EEG)-based neurofeedback training for brain–computer interface (BCI)
نویسندگان
چکیده
منابع مشابه
Neurofeedback-based motor imagery training for brain-computer interface (BCI).
In the present study, we propose a neurofeedback-based motor imagery training system for EEG-based brain-computer interface (BCI). The proposed system can help individuals get the feel of motor imagery by presenting them with real-time brain activation maps on their cortex. Ten healthy participants took part in our experiment, half of whom were trained by the suggested training system and the o...
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Background and Objective Dry EEG sensors are important for BCI technologies to gain wide-spread long-term use. The gel-based ones present inconveniences: gel is wet, it dries up and itches, requires hair washing afterwards, varying impedances and need for periodical recalibration. The existing dry EEG electrodes (typically arrays of metallic pins) have higher impedances than the wet ones, depen...
متن کاملEEG-based local brain activity feedback training—tomographic neurofeedback
Along with the development of distributed EEG source modeling methods, basic approaches to local brain activity (LBA-) neurofeedback (NF) have been suggested. Meanwhile several attempts using LORETA and sLORETA have been published. This article specifically reports on "EEG-based LBA-feedback training" developed by Bauer et al. (2011). Local brain activity-feedback has the advantage over other s...
متن کاملNeurofeedback and quantitative electroencephalography.
This study was conducted in an attempt to determine the efficacy of neurofeedback (NFB) in the treatment of patients suffering from vertigo or tinnitus. Results indicated that after NFB, power for delta and theta bands was reduced; however, an increase of power was noted for the alpha bands. Furthermore, normalization was observed for the vestibular evoked potentials (VestEP). After NFB, a norm...
متن کاملA review of classification algorithms for EEG-based brain–computer interfaces
In this paper we review classification algorithms used to design BrainComputer Interface (BCI) systems based on ElectroEncephaloGraphy (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI. PACS...
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ژورنال
عنوان ژورنال: Experimental Brain Research
سال: 2014
ISSN: 0014-4819,1432-1106
DOI: 10.1007/s00221-014-3860-x