True Zero-Training Brain-Computer Interfacing – An Online Study

Abstract

Despite several approaches to realize subject-to-subject transfer of pre-trained classifiers, the full performance of a Brain-Computer Interface (BCI) for a novel user can only be reached by presenting the BCI system with data from the novel user. In typical state-of-the-art BCI systems with a supervised classifier, the labeled data is collected during a calibration recording, in which the user is asked to perform a specific task. Based on the known labels of this recording, the BCI's classifier can learn to decode the individual's brain signals. Unfortunately, this calibration recording consumes valuable time. Furthermore, it is unproductive with respect to the final BCI application, e.g. text entry. Therefore, the calibration period must be reduced to a minimum, which is especially important for patients with a limited concentration ability. The main contribution of this manuscript is an online study on unsupervised learning in an auditory event-related potential (ERP) paradigm. Our results demonstrate that the calibration recording can be bypassed by utilizing an unsupervised trained classifier, that is initialized randomly and updated during usage. Initially, the unsupervised classifier tends to make decoding mistakes, as the classifier might not have seen enough data to build a reliable model. Using a constant re-analysis of the previously spelled symbols, these initially misspelled symbols can be rectified posthoc when the classifier has learned to decode the signals. We compare the spelling performance of our unsupervised approach and of the unsupervised posthoc approach to the standard supervised calibration-based dogma for n = 10 healthy users. To assess the learning behavior of our approach, it is unsupervised trained from scratch three times per user. Even with the relatively low SNR of an auditory ERP paradigm, the results show that after a limited number of trials (30 trials), the unsupervised approach performs comparably to a classic supervised model.

Heading for new shores! Overcoming pitfalls in BCI design

“…The performance, however, drops considerably when moving out of the lab and into end users’ homes [49]. Some studies have shown EP-based BCIs that yield good performance with subject-independent classifiers, i.e., without calibration at all for a new user [78, 79]. However, so far these approaches have mainly been tested in offline studies and with healthy users only.…”

“…It is therefore still unknown whether this results generalize to end-users. Moreover, performances are still better when exploiting subject-specific data and user training, even for P300-based BCI [79, 80]. …”

Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review

“…The ability to use a BCI without calibration or after a short calibration has been recognized in the community as a priority for improving BCI usability. Toward this objective effort is put in the conception, analysis and testing of generic model classifiers and/or domain adaptation methods, allowing the so-called transfer learning, whereas data from other sessions and/or other subjects are used to initialize a BCI so as to start using it without calibration and also to increase the performance of low-performance users [25,30,[57][58][59][60][61][62][63].…”

“…It also allows keeping optimal performance by adapting to mental and environmental changes during the session, enabling continuous adjustments ("pursuing"), thus ensuring reliability and robustness throughout the session and across-sessions [30,62,[64][65][66].…”

A User-Friendly Dictionary-Supported SSVEP-based BCI Application

“…Auto-calibration procedures allow expert-independent home-use of a BCI as they allow people with no technical knowledge to set up the system. Though such calibration methods are common for other BCI approaches [10,13,15,16], they are rarely used for BCIs based on the SSVEP paradigm. However, in the here presented dictionary supported SSVEP-based BCI, a modified version of our earlier developed calibration methods [9] was integrated to set up user specific stimulation frequencies and other key parameters associated with the utilized classification methods such as frequency dependent classification thresholds.…”