Evaluation of Different EEG Acquisition Systems Concerning Their Suitability for Building a Brain–Computer Interface: Case Studies

Pinegger, Andreas; Wriessnegger, Selina; Faller, Josef; Müller-Putz, Gernot

doi:10.3389/fnins.2016.00441

Cited by 42 publications

(45 citation statements)

References 41 publications

(50 reference statements)

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“…In order to elicit P300, the arrangement of visual stimuli in the form of a matrix has been the most commonly used. The matrix-based paradigm to present stimuli originally designed for a P300 speller [14] has also been used for the purpose of controlling environmental devices, replacing spelling characters with icons associated with device control functions [15][16][17][18][19][20][21][22]. Among studies which chose the matrix-based paradigm for their P300 BCIs, some studies especially considered a real-life situation when using P300 BCI for the purpose of environmental control.…”

Section: Introductionmentioning

confidence: 99%

“…However, previously developed BCIs for environmental control required a separate display to provide visual stimuli [15][16][17]19,21,22,[28][29][30], and the display only presents control icons as visual stimuli. In such a system environment, users are not able to see the real devices they are controlling and find it difficult to recognize instantly whether the devices operate as intended.…”

Section: Introductionmentioning

confidence: 99%

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Online Home Appliance Control Using EEG-Based Brain–Computer Interfaces

Kim

Hwang

et al. 2019

Electronics

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Brain–computer interfaces (BCIs) allow patients with paralysis to control external devices by mental commands. Recent advances in home automation and the Internet of things may extend the horizon of BCI applications into daily living environments at home. In this study, we developed an online BCI based on scalp electroencephalography (EEG) to control home appliances. The BCI users controlled TV channels, a digital door-lock system, and an electric light system in an unshielded environment. The BCI was designed to harness P300 and N200 components of event-related potentials (ERPs). On average, the BCI users could control TV channels with an accuracy of 83.0% ± 17.9%, the digital door-lock with 78.7% ± 16.2% accuracy, and the light with 80.0% ± 15.6% accuracy, respectively. Our study demonstrates a feasibility to control multiple home appliances using EEG-based BCIs.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Online Home Appliance Control Using EEG-Based Brain–Computer Interfaces

Kim

Hwang

et al. 2019

Electronics

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“…The therapist can then spend more time on other types of training and patients, or the patients could be training in their own home. Many of the caps currently commercially available may be difficult to mount by oneself since the electrodes must cover the motor cortex to record the electrical activity associated with attempted movements, and only a few comparisons between headsets or headset usability have been made (Ekandem et al, 2012;Mayaud et al, 2013;Das et al, 2014;Hairston et al, 2014;Nijboer et al, 2015;Halford et al, 2016;Izdebski et al, 2016;Pinegger et al, 2016;Käthner et al, 2017;Zander et al, 2017;Radüntz and Meffert, 2019). These studies relied on different metrics but often report on comfort and setup time.…”

Section: Introductionmentioning

confidence: 99%

“…Other aspects covered by them include the unobtrusiveness (Das et al, 2014) and design elements such as adaptability of head sizes, external trigger integration, cap fit, and variance in scalp electrode locations (Hairston et al, 2014;Izdebski et al, 2016). Moreover, the satisfaction using questionnaires and system preferences are reported as well (Hairston et al, 2014;Nijboer et al, 2015;Pinegger et al, 2016;Käthner et al, 2017). Another aspect to consider is the aesthetics of the headset, which has been reported to be important (Nijboer et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of EEG Headset Mounting for Brain-Computer Interface-Based Stroke Rehabilitation by Patients, Therapists, and Relatives

Jochumsen

Knoche

Kidmose

et al. 2020

Front. Hum. Neurosci.

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Brain-computer interfaces (BCIs) have successfully been used for motor recovery training in stroke patients. However, the setup of BCI systems is complex and may be divided into (1) mounting the headset and (2) calibration of the BCI. One of the major problems is mounting the headset for recording brain activity in a stroke rehabilitation context, and usability testing of this is limited. In this study, the aim was to compare the translational aspects of mounting five different commercially available headsets from a user perspective and investigate the design considerations associated with technology transfer to rehabilitation clinics and home use. No EEG signals were recorded, so the effectiveness of the systems have not been evaluated. Three out of five headsets covered the motor cortex which is needed to pick up movement intentions of attempted movements. The other two were as control and reference for potential design considerations. As primary stakeholders, nine stroke patients, eight therapists and two relatives participated; the stroke patients mounted the headsets themselves. The setup time was recorded, and participants filled in questionnaires related to comfort, aesthetics, setup complexity, overall satisfaction, and general design considerations. The patients had difficulties in mounting all headsets except for a headband with a dry electrode located on the forehead (control). The therapists and relatives were able to mount all headsets. The fastest headset to mount was the headband, and the most preferred headsets were the headband and a behind-ear headset (control). The most preferred headset that covered the motor cortex used water-based electrodes. The patients reported that it was important that they could mount the headset themselves for them to use it every day at home. These results have implications for design considerations for the development of BCI systems to be used in rehabilitation clinics and in the patient's home.

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“…BCI failure has been attributed to several main aspects. Some say BCIs do not have a sufficient control method for measuring brain signals, or that the hardware for measuring brain signals is insufficient (Pinegger et al, 2016 ), while others argue that BCIs are not reliable or robust because of inter-user variability (Jeunet et al, 2016 ). The accuracy of BCI-controlled prosthetics/orthotics needs to improve before patients can reliably control a BCI with a sense of agency.…”

Section: Introductionmentioning

confidence: 99%

Classification of Movement and Inhibition Using a Hybrid BCI

et al. 2017

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Brain-computer interfaces (BCIs) are an emerging technology that are capable of turning brain electrical activity into commands for an external device. Motor imagery (MI)—when a person imagines a motion without executing it—is widely employed in BCI devices for motor control because of the endogenous origin of its neural control mechanisms, and the similarity in brain activation to actual movements. Challenges with translating a MI-BCI into a practical device used outside laboratories include the extensive training required, often due to poor user engagement and visual feedback response delays; poor user flexibility/freedom to time the execution/inhibition of their movements, and to control the movement type (right arm vs. left leg) and characteristics (reaching vs. grabbing); and high false positive rates of motion control. Solutions to improve sensorimotor activation and user performance of MI-BCIs have been explored. Virtual reality (VR) motor-execution tasks have replaced simpler visual feedback (smiling faces, arrows) and have solved this problem to an extent. Hybrid BCIs (hBCIs) implementing an additional control signal to MI have improved user control capabilities to a limited extent. These hBCIs either fail to allow the patients to gain asynchronous control of their movements, or have a high false positive rate. We propose an immersive VR environment which provides visual feedback that is both engaging and immediate, but also uniquely engages a different cognitive process in the patient that generates event-related potentials (ERPs). These ERPs provide a key executive function for the users to execute/inhibit movements. Additionally, we propose signal processing strategies and machine learning algorithms to move BCIs toward developing long-term signal stability in patients with distinctive brain signals and capabilities to control motor signals. The hBCI itself and the VR environment we propose would help to move BCI technology outside laboratory environments for motor rehabilitation in hospitals, and potentially for controlling a prosthetic.

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Evaluation of Different EEG Acquisition Systems Concerning Their Suitability for Building a Brain–Computer Interface: Case Studies

Cited by 42 publications

References 41 publications

Online Home Appliance Control Using EEG-Based Brain–Computer Interfaces

Online Home Appliance Control Using EEG-Based Brain–Computer Interfaces

Evaluation of EEG Headset Mounting for Brain-Computer Interface-Based Stroke Rehabilitation by Patients, Therapists, and Relatives

Classification of Movement and Inhibition Using a Hybrid BCI

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