147 research outputs found
What we can and cannot (yet) do with functional near infrared spectroscopy
Functional near infrared spectroscopy (NIRS) is a relatively new technique complimentary to EEG for the development of brain-computer interfaces (BCIs). NIRS-based systems for detecting various cognitive and affective states such as mental and emotional stress have already been demonstrated in a range of adaptive human–computer interaction (HCI) applications. However, before NIRS-BCIs can be used reliably in realistic HCI settings, substantial challenges oncerning signal processing and modeling must be addressed. Although many of those challenges have been identified previously, the solutions to overcome them remain scant. In this paper, we first review what can be currently done with NIRS, specifically, NIRS-based approaches to measuring cognitive and affective user states as well as demonstrations of passive NIRS-BCIs. We then discuss some of the primary challenges these systems would face if deployed in more realistic settings, including detection latencies and motion artifacts. Lastly, we investigate the effects of some of these challenges on signal reliability via a quantitative comparison of three NIRS models. The hope is that this paper will actively engage researchers to acilitate the advancement of NIRS as a more robust and useful tool to the BCI community
Trade-off between reconstruction loss and feature alignment for domain generalization
Domain generalization (DG) is a branch of transfer learning that aims to
train the learning models on several seen domains and subsequently apply these
pre-trained models to other unseen (unknown but related) domains. To deal with
challenging settings in DG where both data and label of the unseen domain are
not available at training time, the most common approach is to design the
classifiers based on the domain-invariant representation features, i.e., the
latent representations that are unchanged and transferable between domains.
Contrary to popular belief, we show that designing classifiers based on
invariant representation features alone is necessary but insufficient in DG.
Our analysis indicates the necessity of imposing a constraint on the
reconstruction loss induced by representation functions to preserve most of the
relevant information about the label in the latent space. More importantly, we
point out the trade-off between minimizing the reconstruction loss and
achieving domain alignment in DG. Our theoretical results motivate a new DG
framework that jointly optimizes the reconstruction loss and the domain
discrepancy. Both theoretical and numerical results are provided to justify our
approach.Comment: 13 pages, 2 table
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