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This Research Topic combines articles aiming to gain a better understanding on different factors that determine whether people are successful or not in controlling computerized devices with brain signals. Since decades, technological advancements in neuroscience allow the interpretation of brain signals and their translation into control messages (Brain-computer interface (BCI)). Moreover, the control of brain signals can be used to induce changes in cognition and behavior (Neurofeedback (NF)). However, the break-through of this technology for the broad population in real-world applications has not yet arrived. Various factors have been related to the individual success in controlling computerized devices with brain signals, but to date, no general theoretical framework is available. In this Research Topic, aspects of the training protocol such as instructions, task and feedback as well as cognitive and psychological traits such as motivation, mood, locus of control and empathy are investigated as determinants of BCI or NF performance. Moreover, the mechanisms and networks involved in gaining and maintaining control over brain activity as well as its prediction are addressed. Finally, as the ultimate goal of this research is to use BCI and NF for communication or control and therapy, respectively, novel applications for individuals with disabilities or disorders are discussed.
brain-computer interface (BCI) --- neurofeedback (NF) --- electroencephalogramm (EEG) --- (functional) magnetic resonance imaging ((f)MRI) --- repetitive transcranial magnetic stimulation (rTMS) --- training protocol --- psychological traits --- control and its prediction --- applications for disabled individuals
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The field of Brain–Computer Interfaces (BCIs) has grown rapidly in the last few decades, allowing the development of faster and more reliable assistive technologies based on direct links between the brain and an external device. Novel applications of BCIs have also been proposed, especially in the area of human augmentation, i.e., enabling people to go beyond human limitations in sensory, cognitive and motor tasks. Brain-imaging techniques, such as electroencephalography, have been used to extract neural correlates of various brain processes and transform them, via machine learning, into commands for external devices. Brain stimulation technology has allowed to trigger the activation of specific brain areas to enhance the cognitive processes associated to the task at hand, hence improving performance. BCIs have therefore extended their scope from assistive technologies for people with disabilities to neuro-tools for human enhancement. This Special Issue aims at showing the recent advances in BCIs for human augmentation, highlighting new results on both traditional and novel applications. These include, but are not limited to, control of external devices, communication, cognitive enhancement, decision making and entertainment.
Brain–Computer Interface (BCI) --- speller --- Graphical User Interface (GUI) --- SSVEP --- P300 --- MI --- hybrid --- human performance --- performance prediction --- indoor room temperature --- office-work tasks --- electroencephalography (EEG) --- brain computer interface --- complete locked-in state --- communication --- Artificial Neural Network --- 20-questions-game --- augmented cognition --- brain–computer interfaces --- superintelligence --- heuristic search --- electroencephalography --- brain-computer interfaces --- waveform --- p300 --- SIFT --- PE --- MP --- SHCC --- n/a
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