Utilizing Secondary Input from Passive Brain-Computer Interfaces for Enhancing Human-Machine Interaction

Zander, Thorsten O.; Kothe, Christian; Welke, Sebastian; Roetting, Matthias

doi:10.1007/978-3-642-02812-0_86

Cited by 57 publications

(47 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It also summarizes the behavior of the different groups, by displaying the metaspelling accuracy corresponding to the whole group, the low specificity group and the high specificity group, respectively. For those three groups, it further emphasizes the boundaries given by (3), that is, the minimum required trade-off between specificity and sensitivity. Precisely, given the observed GCR and spelling accuracy (in the absence of any correction) for each group, each boundary represents the limit above which the automatic correction becomes fruitful.…”

Section: Performance In Automatic Errormentioning

confidence: 98%

“…In this context, it is highly relevant to look for a way to detect and correct errors. One way to tackle this issue is to appeal to the hybrid BCI approach [2], where it has been shown that BCI performance could be improved by supplementing the firstorder brain signal with second-level information to aid the primary classifier and to improve the final decision or BCI output [3]. This complementary signal can be either of a cerebral origin or of a very different nature [2].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Objective and Subjective Evaluation of Online Error Correction during P300-Based Spelling

Margaux

Maby

Daligault

et al. 2012

Advances in Human-Computer Interaction

109

View full text Add to dashboard Cite

Error potentials (ErrP) are alterations of EEG traces following the subject's perception of erroneous feedbacks. They provide a way to recognize misinterpreted commands in brain-computer interfaces (BCI). However, this has been evaluated online in only a couple of studies and mostly with very few subjects. In this study, we implemented a P300-based BCI, including not only online error detection but also, for the first time, automatic correction. We evaluated it in 16 healthy volunteers. Whenever an error was detected, a new decision was made based on the second best guess of a probabilistic classifier. At the group level, correction did neither improve nor deteriorate spelling accuracy. However, automatic correction yielded a higher bit rate than a respelling strategy. Furthermore, the fine examination of interindividual differences in the efficiency of error correction and spelling clearly distinguished between two groups who differed according to individual specificity in ErrP detection. The high specificity group had larger evoked responses and made fewer errors which were corrected more efficiently, yielding a 4% improvement in spelling accuracy and a higher bit rate. Altogether, our results suggest that the more the subject is engaged into the task, the more useful and well accepted the automatic error correction.

show abstract

Section: Performance In Automatic Errormentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Objective and Subjective Evaluation of Online Error Correction during P300-Based Spelling

Margaux

Maby

Daligault

et al. 2012

Advances in Human-Computer Interaction

109

View full text Add to dashboard Cite

show abstract

“…In particular, such changes can be used to monitor the success of motor-learning and the impact of therapy on the patient. The detectability of different aspects of cognitive user state, like cognitive load [37], perception of errors [38], the perceived loss of control [39], and vigilance [40], has already been shown in general human-machine systems [41], and may find applications in human-robot interaction for stroke rehabilitation. For example, this information could be fed back to the user via neurofeedback to induce mental states beneficial to successful motor learning.…”

Section: Cognitive Monitoring During Patient-robot Interactionmentioning

confidence: 98%

A brain-robot interface for studying motor learning after stroke

Meyer

Peters

Brtz

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

Abstract-Despite intensive efforts, no significant benefit of rehabilitation robotics in post-stroke motor-recovery has yet been demonstrated in large-scale clinical trials. The present work is based on the premise that future advances in rehabilitation robotics require an enhanced understanding of the neural processes involved in motor learning after stroke. We present a system that combines a Barret WAM TM seven degreeof-freedom robot arm with neurophysiological recordings for the purpose of studying post-stroke motor learning. We used this system to conduct a pilot study on motor learning during reaching movements with two stroke patients. Preliminary results indicate that pre-trial brain activity in ipsilesional sensorimotor areas may be a neural correlate of the current state of motor learning. These results are discussed in terms of their relevance for future rehabilitation strategies that combine rehabilitation robotics with real-time analyses of neurophysiological recordings.

show abstract

“…In contrast, passive BCIs are based on "reactive states of the user's cognition automatically induced while interacting in the surrounding system" [44]. Passive inputs assess user state and use that to help control interaction without direct or intentional effort from the user.…”

Section: Passive Brain-computer Interfacesmentioning

confidence: 99%

Dynamic difficulty using brain metrics of workload

Afergan

Peck

Solovey

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

119

View full text Add to dashboard Cite

Dynamic difficulty adjustments can be used in humancomputer systems in order to improve user engagement and performance. In this paper, we use functional near-infrared spectroscopy (fNIRS) to obtain passive brain sensing data and detect extended periods of boredom or overload. From these physiological signals, we can adapt a simulation in order to optimize workload in real-time, which allows the system to better fit the task to the user from moment to moment. To demonstrate this idea, we ran a laboratory study in which participants performed path planning for multiple unmanned aerial vehicles (UAVs) in a simulation. Based on their state, we varied the difficulty of the task by adding or removing UAVs and found that we were able to decrease errors by 35% over a baseline condition. Our results show that we can use fNIRS brain sensing to detect task difficulty in real-time and construct an interface that improves user performance through dynamic difficulty adjustment.

show abstract

Utilizing Secondary Input from Passive Brain-Computer Interfaces for Enhancing Human-Machine Interaction

Cited by 57 publications

References 13 publications

Objective and Subjective Evaluation of Online Error Correction during P300-Based Spelling

Objective and Subjective Evaluation of Online Error Correction during P300-Based Spelling

A brain-robot interface for studying motor learning after stroke

Dynamic difficulty using brain metrics of workload

Contact Info

Product

Resources

About