Defining brain–machine interface applications by matching interface performance with device requirements

Tonet, O.; Marinelli, Martina; Citi, Luca; Rossini, Paolo Maria; Rossini, Luca; Megali, Giuseppe; Dario, P.

doi:10.1016/j.jneumeth.2007.03.015

Cited by 69 publications

(48 citation statements)

References 112 publications

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“…The ability to produce outputs within a reasonable time window is a prerequisite for online systems to be applied in everyday life where responsiveness is needed [46]. So, while the speed up is not so important in our offline validation of the system, this is an added bonus as it makes the cBCI ready for future online experimentation.…”

Section: Contributionsmentioning

confidence: 99%

Enhancement of Group Perception via a Collaborative Brain–Computer Interface

Valeriani

Poli

Cinel

2017

IEEE Trans. Biomed. Eng.

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Abstract-Objective. We aimed at improving group performance in a challenging visual search task via a hybrid Collaborative Brain-Computer Interface (cBCI). Methods. Ten participants individually undertook a visual search task where a display was presented for 250 ms, and they had to decide whether a target was present or not. Local Temporal Correlation Common Spatial Pattern (LTCCSP) was used to extract neural features from response-and stimulus-locked EEG epochs. The resulting feature vectors were extended by including response times and features extracted from eye movements. A classifier was trained to estimate the confidence of each group member. cBCI-assisted group decisions were then obtained using a confidence-weighted majority vote. Results. Participants were combined in groups of different sizes to assess the performance of the cBCI. Results show that LTCCSP neural features and eye movements features significantly improve the accuracy of the cBCI over what we achieved with previous systems. For most group sizes, our hybrid cBCI yields group decisions that are significantly better than majority-based group decisions. Conclusion. The visual task considered here was much harder than a task we used in previous research. However, thanks to a range of technological enhancements, our cBCI has delivered a significant improvement over group decisions made by a standard majority vote. Significance. With previous cBCIs groups may perform better than single non-BCI users. Here, cBCI-assisted groups are more accurate than identically-sized non-BCI groups. This paves the way to a variety of real-world applications of cBCIs where reducing decision errors is vital.

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Section: Contributionsmentioning

confidence: 99%

Enhancement of Group Perception via a Collaborative Brain–Computer Interface

Valeriani

Poli

Cinel

2017

IEEE Trans. Biomed. Eng.

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“…While 3 degree of sensing myoelectric prosthetics typically require months of intensive training, comparable comparable to training and recovery times for invasive BMI approaches for many-DoF robotic arms [12], in this paper we demonstrate that users can generate letters within minutes of first time use of our 6 DoF teleprosthetic. Powered continuous wheelchair control requires 15 bit/s and full-finger hand prosthetics 54 bit/s, well beyond the reported performance of current noninvasive BMI approaches (EEG & EMG < 3 bit/s) [13]. The band-with limitation is mainly due to limitations set by the variability and complexity of the physiological signal noise and sensor noise [14] that are orders of magnitude higher than noise in calibrating and recording eye movementsMoreover, both invasive and non-invasive BMI approaches come at significant clinical and equipment costs.…”

Section: Discussionmentioning

confidence: 99%

Gaze-based teleprosthetic enables intuitive continuous control of complex robot arm use: Writing & drawing

Dziemian¹,

Abbott²,

Faisal

2016

2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob)

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Abstract-Eye tracking is a powerful mean for assistive technologies for people with movement disorders, paralysis and amputees. We present a highly intuitive eye tracking-controlled robot arm operating in 3-dimensional space based on the user's gaze target point that enables tele-writing and drawing. The usability and intuitive usage was assessed by a "tele"writing experiment with 8 subjects that learned to operate the system within minutes of first time use. These subjects were naive to the system and the task and had to write three letters on a white board with a white board pen attached to the robot arm's endpoint. The instructions are to imagine they were writing text with the pen and look where the pen would be going, they had to write the letters as fast and as accurate as possible, given a letter size template. Subjects were able to perform the task with facility and accuracy, and movements of the arm did not interfere with subjects ability to control their visual attention so as to enable smooth writing. On the basis of five consecutive trials there was a significant decrease in the total time used and the total number of commands sent to move the robot arm from the first to the second trial but no further improvement thereafter, suggesting that within writing 6 letters subjects had mastered the ability to control the system. Our work demonstrates that eye tracking is a powerful means to control robot arms in closed-loop and real-time, outperforming other invasive and non-invasive approaches to Brain-MachineInterfaces in terms of calibration time (<2 minutes), training time (<10 minutes), interface technology costs. We suggests that gaze-based decoding of action intention may well become one of the most efficient ways to interface with robotic actuators -i.e. Brain-Robot-Interfaces -and become useful beyond paralysed and amputee users also for the general teleoperation of robotic and exoskeleton in human augmentation.

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“…The tasks (by convention indicated by the name of the corresponding brain activity) that are currently most popular are: slow cortical potentials, imaginary movement, P300, and steady-state visuallyevoked potentials [15,17]. Users regularly indicate that these tasks are either too slow, nonintuitive, cumbersome, or just annoying to use for control BCIs [10,12].…”

Section: User-centered Design: What Users Wantmentioning

confidence: 99%

A Study in User-Centered Design and Evaluation of Mental Tasks for BCI

Bos

Poel

Nijholt

2011

Lecture Notes in Computer Science

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Abstract. Current brain-computer interfacing (BCI) research focuses on detection performance, speed, and bit rates. However, this is only a small part of what is important to the user. From human-computer interaction (HCI) research, we can apply the paradigms of user-centered design and evaluation, to improve the usability and user experience. Involving the users in the design process may also help in moving beyond the limited mental tasks that are currently common in BCI systems. To illustrate the usefulness of these methods to BCI, we involved potential users in the design process of a BCI system, resulting in three new mental tasks. The experience of using these mental tasks was then evaluated within a prototype BCI system using a commercial online role-playing game. Results indicate that user preference for certain mental tasks is primarily based on the recognition of brain activity by the system, and secondly on the ease of executing the task.

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Defining brain–machine interface applications by matching interface performance with device requirements

Cited by 69 publications

References 112 publications

Enhancement of Group Perception via a Collaborative Brain–Computer Interface

Enhancement of Group Perception via a Collaborative Brain–Computer Interface

Gaze-based teleprosthetic enables intuitive continuous control of complex robot arm use: Writing & drawing

A Study in User-Centered Design and Evaluation of Mental Tasks for BCI

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