Loading...
Please wait, while we are loading the content...
Similar Documents
Hyperdimensional computing for noninvasive brain-computer interfaces: Blind and one-shot classification of EEG error-related potentials
| Content Provider | Semantic Scholar |
|---|---|
| Author | Rahimi, Abbas Kanerva, Pentti Millán, José Del R. Rabaey, Jan M. |
| Copyright Year | 2017 |
| Abstract | The mathematical properties of high-dimensional (HD) spaces show remarkable agreement with behaviors controlled by the brain. Computing with HD vectors, referred to as "hypervectors," is a brain-inspired alternative to computing with numbers. HD computing is characterized by generality, scalability, robustness, and fast learning, making it a prime candidate for utilization in application domains such as brain-computer interfaces. We describe the use of HD computing to classify electroencephalography (EEG) error-related potentials for noninvasive brain-computer interfaces. Our algorithm encodes neural activity recorded from 64 EEG electrodes to a single temporal-spatial hypervector. This hypervector represents the event of interest and is used for recognition of the subject's intentions. Using the full set of training trials, HD computing achieves on average 5% higher accuracy compared to a conventional machine learning method on this task (74.5% vs. 69.5%) and offers further advantages: (1) Our algorithm learns fast by using 34% of training trials while surpassing the conventional method with an average accuracy of 70.5%. (2) Conventional method requires prior domain expert knowledge to carefully select a subset of electrodes for a subsequent pre-processor and classier, whereas our algorithm blindly uses all 64 electrodes, tolerates noises in data, and the resulting hypervector is intrinsically clustered into HD space; in addition, most preprocessing of the electrode signal can be eliminated while maintaining an average accuracy of 71.7%. |
| File Format | PDF HTM / HTML |
| DOI | 10.4108/eai.22-3-2017.152397 |
| Alternate Webpage(s) | https://iis-people.ee.ethz.ch/~arahimi/papers/BICT17.pdf |
| Alternate Webpage(s) | https://infoscience.epfl.ch/record/229909/files/BICT17.pdf |
| Alternate Webpage(s) | http://eudl.eu/pdf/10.4108/eai.22-3-2017.152397 |
| Alternate Webpage(s) | https://doi.org/10.4108/eai.22-3-2017.152397 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
