2017
DOI: 10.1007/s11036-017-0942-6
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Hyperdimensional Computing for Blind and One-Shot Classification of EEG Error-Related Potentials

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Cited by 51 publications
(41 citation statements)
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“…We also observe that HD computing quickly learns from one or two seizures and perfectly detects unseen seizures for the majority of patients (10 out of 16) [19]. Similar benefit is observed for the EEG ERP classification task [17], [18]: the HD algorithm learns ≈ 3× faster by using only 34% of training trials while maintaining an average accuracy of 70.5%, which is higher than the state-of-the-art classifier using the full set of training trials.…”
Section: Learning Is One-shot Fast and Computationally Balanced supporting
confidence: 71%
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“…We also observe that HD computing quickly learns from one or two seizures and perfectly detects unseen seizures for the majority of patients (10 out of 16) [19]. Similar benefit is observed for the EEG ERP classification task [17], [18]: the HD algorithm learns ≈ 3× faster by using only 34% of training trials while maintaining an average accuracy of 70.5%, which is higher than the state-of-the-art classifier using the full set of training trials.…”
Section: Learning Is One-shot Fast and Computationally Balanced supporting
confidence: 71%
“…Besides, the learned HD vectors can be analyzed to identify what electrodes provide meaningful data for the classification. It has been shown that instead of asking for the domain expert knowledge, HD computing can identify the same subset of electrodes as relevant by measuring the relative distances between the learned prototype HD vectors [18]. Producing such transparent codes also enables verification of the learned model [52], and is in sharp contrast to blind application of conventional learning methods that produce a "black box.…”
Section: B Learning Transparent Codes With Interpretable Featuresmentioning
confidence: 99%
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“…Based on the use of these MAP operations, an encoder can be designed for various tasks, e.g., EMG [20], [22], [49], EEG [23], [50], ECoG [51], ExG [45], or in general pattern processing [52]. The encoder emits a hypervector representing the event of interest that is then fed into an associative memory (AM) for training and inference.…”
Section: B Hyperdimensional Computingmentioning
confidence: 99%
“…The proposed system is highly flexible and versatile, being capable of adapting to the changes in the initial setup and other interferences that can deteriorate the performance. Moreover, it can be easily adapted to other kinds of applications, where a higher number of acquisition channels is required, both for EEG [23] and EMG [22] based applications. The system reaches 90.40% accuracy on 11 gestures recognition, comparable to the SoA systems, within an energy budget of just 10.04mJ for the onchip training and 83.20µJ for the recognition (inference), leading to an average power consumption of 10.04mW reaching 29 hours battery life with a 100mAh battery.…”
Section: Introductionmentioning
confidence: 99%