Nessi: An EEG-Controlled Web Browser for Severely Paralyzed Patients

We have previously demonstrated that an EEG-controlled web browser based on self-regulation of slow cortical potentials (SCPs) enables severely paralyzed patients to browse the internet independently of any voluntary muscle control. However, this system had several shortcomings, among them that patients could only browse within a limited number of web pages and had to select links from an alphabetical list, causing problems if the link names were identical or if they were unknown to the user (as in graphical links). Here we describe a new EEG-controlled web browser, called Nessi, which overcomes these shortcomings. In Nessi, the open source browser, Mozilla, was extended by graphical in-place markers, whereby different brain responses correspond to different frame colors placed around selectable items, enabling the user to select any link on a web page. Besides links, other interactive elements are accessible to the user, such as e-mail and virtual keyboards, opening up a wide range of hypertext-based applications

Enhancing brain-machine interface (BMI) control of a hand exoskeleton using electrooculography (EOG)

Brain-machine interfaces (BMIs) allow direct translation of electric, magnetic or metabolic brain signals into control commands of external devices such as robots, prostheses or exoskeletons. However, non-stationarity of brain signals and susceptibility to biological or environmental artifacts impede reliable control and safety of BMIs, particularly in daily life environments. Here we introduce and tested a novel hybrid brain-neural computer interaction (BNCI) system fusing electroencephalography (EEG) and electrooculography (EOG) to enhance reliability and safety of continuous hand exoskeleton-driven grasping motions

Brain-Computer Communication

Abstract — A Thought-Translation-Device (TTD) has been designed to enable direct brain-computer communication using self regulation of slow cortical potentials (SCPs). However, accuracy of SCP control reveals high inter-subject variability. To guarantee the highest possible communication speed, some important aspects of training SCPs are discussed. A baseline correction of SCPs can increase performance. Multi-channel recordings show that SCPs are of highest amplitude around the vertex electrode used for feedback but in some subjects more global distributions were observed. A new method for control of eye movement are presented. Sequential effects of trial-to-trial interaction may also cause difficulties to the user. Finally, psychophysiological factors determining SCP-communication are discussed

Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation

We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic (EOG) artifacts into individual components. An implementation of the selected BSS/ICA method with SVMs trained to classify EMG and EOG artifacts, which enables the usage of the method as a filter in measurements with online feedback, is described. This filter is evaluated on three BCI datasets as a proof-of-concept of the method

Source Codes and Software for EEG/EOG B/NHE control used in Soekadar et al. 2016

This data container includes the custom-made modules for EEG/EOG-based B/NHE control that were embedded into the BCI2000 environment used in Soekadar et al. 2016. Please see the included tutorial for instructions on how to install and run the software

Hybrid EEG/EOG-based brain/neural hand exoskeleton restores fully independent daily living activities after quadriplegia

Direct brain control of advanced robotic systems promises substantial improvements in health care, for example, to restore intuitive control of hand movements required for activities of daily living in quadriplegics, like holding a cup and drinking, eating with cutlery, or manipulating different objects. However, such integrated, brain- or neural-controlled robotic systems have yet to enter broader clinical use or daily life environments. We demonstrate full restoration of independent daily living activities, such as eating and drinking, in an everyday life scenario across six paraplegic individuals (five males, 30 ± 14 years) who used a noninvasive, hybrid brain/neural hand exoskeleton (B/NHE) to open and close their paralyzed hand. The results broadly suggest that brain/neural-assistive technology can restore autonomy and independence in quadriplegic individuals’ everyday life