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. 2021 Nov 27;21(23):7908.
doi: 10.3390/s21237908.

Level-K Classification from EEG Signals Using Transfer Learning

Dor Mizrahi  1 , Inon Zuckerman  1 , Ilan Laufer  1
Affiliations

Level-K Classification from EEG Signals Using Transfer Learning

Dor Mizrahi et al. Sensors (Basel). .

Abstract

Tacit coordination games are games in which communication between the players is not allowed or not possible. In these games, the more salient solutions, that are often perceived as more prominent, are referred to as focal points. The level-k model states that players' decisions in tacit coordination games are a consequence of applying different decision rules at different depths of reasoning (level-k). A player at Lk=0 will randomly pick a solution, whereas a Lk≥1 player will apply their strategy based on their beliefs regarding the actions of the other players. The goal of this study was to examine, for the first time, the neural correlates of different reasoning levels in tacit coordination games. To that end, we have designed a combined behavioral-electrophysiological study with 3 different conditions, each resembling a different depth reasoning state: (1) resting state, (2) picking, and (3) coordination. By utilizing transfer learning and deep learning, we were able to achieve a precision of almost 100% (99.49%) for the resting-state condition, while for the picking and coordination conditions, the precision was 69.53% and 72.44%, respectively. The application of these findings and related future research options are discussed.

Keywords: EEG; classification; level-k; tacit coordination; transfer learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure A1
Figure A1
Symlet wavelet function in different orders.
Figure A2
Figure A2
EEG relative band calculation using DWT.
Figure A3
Figure A3
Baseline model windowing scheme.
Figure A4
Figure A4
Four-layer trainable neural network model.
Figure 1
Figure 1
(A) Standby screen. (B) Game board #1 {“Water”, “Beer”, “Wine”, “Whisky”}.
Figure 2
Figure 2
Experimental paradigm with timeline.
Figure 3
Figure 3
Preprocess pipeline.
Figure 4
Figure 4
CWT results in different experimental states (resting, picking, and coordination).
Figure 5
Figure 5
Analysis of Player #3 CWT images as a function of time and CWT scale factor.
Figure 6
Figure 6
One-versus-all classifier architecture.
Figure 7
Figure 7
Transfer learning scheme for binary classifier.
Figure 8
Figure 8
The optimal prediction weights in a 10–20 system.
See this image and copyright information in PMC

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