A HIDDEN MARKOV MODEL FOR THE DETECTION OF PURE AND MIXED STRATEGY PLAY IN GAMES

We propose a statistical model to assess whether individuals strategically use mixed strategies in repeated games. We formulate a hidden Markov model in which the latent state space contains both pure and mixed strategies. We apply the model to data from an experiment in which human subjects repeatedly play a normal form game against a computer that always follows its part of the unique mixed strategy Nash equilibrium profile. Estimated results show significant mixed strategy play and nonstationary dynamics. We also explore the ability of the model to forecast action choice.

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Physical Context Detection using Wearable Wireless Sensor Networks

Journal of Communications Software and Systems ◽

10.24138/jcomss.v4i3.219 ◽

2008 ◽

Vol 4 (3) ◽

pp. 191 ◽

Cited By ~ 17

Author(s):

Muhannad Quwaider ◽

Subir Biswas

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Human Subjects ◽

Real Life ◽

Sensor Data ◽

Physical Context ◽

Significant Extension ◽

The Individual ◽

Stochastic Identification

This paper presents the architecture of a wearable sensor network and a Hidden Markov Model (HMM) processingframework for stochastic identification of body postures andphysical contexts. The key idea is to collect multi-modal sensor data from strategically placed wireless sensors over a human subject’s body segments, and to process that using HMM in order to identify the subject’s instantaneous physical context. The key contribution of the proposed multi-modal approach is a significant extension of traditional uni-modal accelerometry in which only the individual body segment movements, without their relative proximities and orientation modalities, is used for physical context identification. Through real-life experiments with body mounted sensors it is demonstrated that while the unimodal accelerometry can be used for differentiating activityintensive postures such as walking and running, they are not effective for identification and differentiation between lowactivity postures such as sitting, standing, lying down, etc. In the proposed system, three sensor modalities namely acceleration, relative proximity and orientation are used for context identification through Hidden Markov Model (HMM) based stochastic processing. Controlled experiments using human subjects are carried out for evaluating the accuracy of the HMMidentified postures compared to a naïve threshold based mechanism over different human subjects.

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A hidden Markov model reliably characterizes ketamine-induced spectral dynamics in macaque local field potentials and human electroencephalograms

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009280 ◽

2021 ◽

Vol 17 (8) ◽

pp. e1009280

Author(s):

Indie C. Garwood ◽

Sourish Chakravarty ◽

Jacob Donoghue ◽

Meredith Mahnke ◽

Pegah Kahali ◽

...

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Local Field ◽

Hidden Markov ◽

Human Subjects ◽

Local Field Potentials ◽

Field Potentials ◽

State Process ◽

The Mean ◽

Eeg Recordings

Ketamine is an NMDA receptor antagonist commonly used to maintain general anesthesia. At anesthetic doses, ketamine causes high power gamma (25-50 Hz) oscillations alternating with slow-delta (0.1-4 Hz) oscillations. These dynamics are readily observed in local field potentials (LFPs) of non-human primates (NHPs) and electroencephalogram (EEG) recordings from human subjects. However, a detailed statistical analysis of these dynamics has not been reported. We characterize ketamine’s neural dynamics using a hidden Markov model (HMM). The HMM observations are sequences of spectral power in seven canonical frequency bands between 0 to 50 Hz, where power is averaged within each band and scaled between 0 and 1. We model the observations as realizations of multivariate beta probability distributions that depend on a discrete-valued latent state process whose state transitions obey Markov dynamics. Using an expectation-maximization algorithm, we fit this beta-HMM to LFP recordings from 2 NHPs, and separately, to EEG recordings from 9 human subjects who received anesthetic doses of ketamine. Our beta-HMM framework provides a useful tool for experimental data analysis. Together, the estimated beta-HMM parameters and optimal state trajectory revealed an alternating pattern of states characterized primarily by gamma and slow-delta activities. The mean duration of the gamma activity was 2.2s([1.7,2.8]s) and 1.2s([0.9,1.5]s) for the two NHPs, and 2.5s([1.7,3.6]s) for the human subjects. The mean duration of the slow-delta activity was 1.6s([1.2,2.0]s) and 1.0s([0.8,1.2]s) for the two NHPs, and 1.8s([1.3,2.4]s) for the human subjects. Our characterizations of the alternating gamma slow-delta activities revealed five sub-states that show regular sequential transitions. These quantitative insights can inform the development of rhythm-generating neuronal circuit models that give mechanistic insights into this phenomenon and how ketamine produces altered states of arousal.

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A hidden Markov model reliably characterizes ketamine-induced spectral dynamics in macaque LFP and human EEG

10.1101/2020.11.12.20221366 ◽

2020 ◽

Author(s):

Indie C. Garwood ◽

Sourish Chakravarty ◽

Jacob Donoghue ◽

Pegah Kahali ◽

Shubham Chamadia ◽

...

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Human Subjects ◽

Slow Oscillation ◽

State Transitions ◽

State Process ◽

The Mean ◽

Eeg Recordings ◽

Gamma Burst

AbstractKetamine is an NMDA receptor antagonist commonly used to maintain general anesthesia. At anesthetic doses, ketamine causes bursts of 30-50 Hz oscillations alternating with 0.1 to 10 Hz oscillations. These dynamics are readily observed in local field potentials (LFPs) of non-human primates (NHPs) and electroencephalogram (EEG) recordings from human subjects. However, a detailed statistical analysis of these dynamics has not been reported. We characterize ketamine’s neural dynamics using a hidden Markov model (HMM). The HMM observations are sequences of spectral power in 10 Hz frequency bands between 0 to 50 Hz, where power is averaged within each band and scaled between 0 and 1. We model the observations as realizations of multivariate beta probability distributions that depend on a discrete-valued latent state process whose state transitions obey Markov dynamics. Using an expectation-maximization algorithm, we fit this beta-HMM to LFP recordings from 2 NHPs, and separately, to EEG recordings from 9 human subjects who received anesthetic doses of ketamine. Together, the estimated beta-HMM parameters and optimal state trajectory revealed an alternating pattern of states characterized primarily by gamma burst and slow oscillation activity, as well as intermediate states in between. The mean duration of the gamma burst state was 2.5s([1.9,3.4]s) and 1.2s([0.9,1.5]s) for the two NHPs, and 2.7s([1.9,3.8]s) for the human subjects. The mean duration of the slow oscillation state was 1.6s([1.1,2.5]s) and 0.7s([0.6,0.9]s) for the two NHPs, and 2.8s([1.9,4.3]s) for the human subjects. Our beta-HMM framework provides a useful tool for experimental data analysis. Our characterizations of the gamma-burst process offer detailed, quantitative constraints that can inform the development of rhythm-generating neuronal circuit models that give mechanistic insights into this phenomenon and how ketamine produces altered states of arousal.

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