A Shoulder-Surfing Resistant Image-Based Authentication Scheme with a Brain-Computer Interface

Gondesen, Florian; Marx, Matthias; Kycler, Ann-Christine

doi:10.1109/cw.2019.00061

Cited by 2 publications

(1 citation statement)

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“…Numerous published works have reported examples of P300based BMIs for communication and control, including spellers (Kleih et al, 2016;Poletti et al, 2016;Okahara et al, 2017;Flores et al, 2018;Guy et al, 2018;Deligani et al, 2019;Shahriari et al, 2019), authentication systems (Yu et al, 2016;Gondesen et al, 2019), assistive robots (Arrichiello et al, 2017), smart home environments (Achanccaray et al, 2017;Masud et al, 2017;Aydin et al, 2018), neurogames (Venuto et al, 2016), remote vehicles (De Venuto et al, 2017;Nurseitov et al, 2017), wheelchairs (De Venuto et al, 2018), and robotic arms (Tang et al, 2017;Garakani et al, 2019). Because the development of assistive technologies for motor-impaired people is one of the major purposes of BMI research, some groups have evaluated similar applications in clinical environments on people with neurological disorders or reduced mobility.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a P300-Based Brain-Machine Interface for a Robotic Hand-Orthosis Control

et al. 2020

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This work presents the design, implementation, and evaluation of a P300-based brain-machine interface (BMI) developed to control a robotic hand-orthosis. The purpose of this system is to assist patients with amyotrophic lateral sclerosis (ALS) who cannot open and close their hands by themselves. The user of this interface can select one of six targets, which represent the flexion-extension of one finger independently or the movement of the five fingers simultaneously. We tested offline and online our BMI on eighteen healthy subjects (HS) and eight ALS patients. In the offline test, we used the calibration data of each participant recorded in the experimental sessions to estimate the accuracy of the BMI to classify correctly single epochs as target or non-target trials. On average, the system accuracy was 78.7% for target epochs and 85.7% for non-target trials. Additionally, we observed significant P300 responses in the calibration recordings of all the participants, including the ALS patients. For the BMI online test, each subject performed from 6 to 36 attempts of target selections using the interface. In this case, around 46% of the participants obtained 100% of accuracy, and the average online accuracy was 89.83%. The maximum information transfer rate (ITR) observed in the experiments was 52.83 bit/min, whereas that the average ITR was 18.13 bit/min. The contributions of this work are the following. First, we report the development and evaluation of a mind-controlled robotic hand-orthosis for patients with ALS. To our knowledge, this BMI is one of the first P300-based assistive robotic devices with multiple targets evaluated on people with ALS. Second, we provide a database with calibration data and online EEG recordings obtained in the evaluation of our BMI. This data is useful to develop and compare other BMI systems and test the processing pipelines of similar applications.

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