Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Effective Connectivity-Based Neural Decoding: A Causal
  Interaction-Driven Approach   We propose a geometric model-free causality measurebased on multivariate
delay embedding that can efficiently detect linear and nonlinear causal
interactions between time series with no prior information. We then exploit the
proposed causal interaction measure in real MEG data analysis. The results are
used to construct effective connectivity maps of brain activity to decode
different categories of visual stimuli. Moreover, we discovered that the
MEG-based effective connectivity maps as a response to structured images
exhibit more geometric patterns, as disclosed by analyzing the evolution of
toplogical structures of the underlying networks using persistent homology.
Extensive simulation and experimental result have been carried out to
substantiate the capabilities of the proposed approach.

7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph