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Electrophysiology-based detection of emergency braking intention in real-world driving.
| Content Provider | Semantic Scholar |
|---|---|
| Author | Haufe, Stefan Kim, Jeong-Woo Sonnleitner, Andreas Schrauf, Michael Curio, Gabriel Blankertz, Benjamin |
| Copyright Year | 2014 |
| Abstract | OBJECTIVE The fact that all human action is preceded by brain processes partially observable through neuroimaging devices such as electroencephalography (EEG) is currently being explored in a number of applications. A recent study by Haufe et al (2011 J. Neural Eng. 8 056001) demonstrates the possibility of performing fast detection of forced emergency brakings during driving based on EEG and electromyography, and discusses the use of such neurotechnology for braking assistance systems. Since the study was conducted in a driving simulator, its significance regarding real-world applicability needs to be assessed. APPROACH Here, we replicate that experimental paradigm in a real car on a non-public test track. MAIN RESULTS Our results resemble those of the simulator study, both qualitatively (in terms of the neurophysiological phenomena observed and utilized) and quantitatively (in terms of the predictive improvement achievable using electrophysiology in addition to behavioral measures). Moreover, our findings are robust with respect to a temporary secondary auditory task mimicking verbal input from a fellow passenger. SIGNIFICANCE Our study serves as a real-world verification of the feasibility of electrophysiology-based detection of emergency braking intention as proposed in Haufe et al (2011 J. Neural Eng. 8 056001). |
| Starting Page | 056011 |
| Ending Page | 056011 |
| Page Count | 1 |
| File Format | PDF HTM / HTML |
| DOI | 10.1088/1741-2560/11/5/056011 |
| Alternate Webpage(s) | http://iopscience.iop.org/1741-2552/11/5/056011/media/JNE498252suppdata.pdf |
| PubMed reference number | 25111850 |
| Alternate Webpage(s) | https://doi.org/10.1088/1741-2560%2F11%2F5%2F056011 |
| Journal | Medline |
| Volume Number | 11 |
| Issue Number | 5 |
| Journal | Journal of neural engineering |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
