Magnetoencephalography (MEG) source localization method with temporal smoothness constraint based on high-dimensional state space model

2010 ◽  
Vol 68 ◽  
pp. e333
Author(s):  
Makoto Fukushima ◽  
Okito Yamashita ◽  
Shin Ishii ◽  
Mitsuo Kawato ◽  
Masa-aki Sato
Keyword(s):  
State Space ◽  
Source Localization ◽  
State Space Model ◽  
High Dimensional ◽  
Localization Method ◽  
Space Model ◽  
Smoothness Constraint ◽  
Dimensional State Space

scholarly journals LSTM-Guided Coaching Assistant for Table Tennis Practice

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4112 ◽  
Author(s):  
Se-Min Lim ◽  
Hyeong-Cheol Oh ◽  
Jaein Kim ◽  
Juwon Lee ◽  
Jooyoung Park
Keyword(s):  
Time Series ◽  
State Space ◽  
Time Series Data ◽  
State Space Model ◽  
Skill Assessment ◽  
Series Data ◽  
High Dimensional ◽  
Table Tennis ◽  
Space Model ◽  
Low Dimensional

Recently, wearable devices have become a prominent health care application domain by incorporating a growing number of sensors and adopting smart machine learning technologies. One closely related topic is the strategy of combining the wearable device technology with skill assessment, which can be used in wearable device apps for coaching and/or personal training. Particularly pertinent to skill assessment based on high-dimensional time series data from wearable sensors is classifying whether a player is an expert or a beginner, which skills the player is exercising, and extracting some low-dimensional representations useful for coaching. In this paper, we present a deep learning-based coaching assistant method, which can provide useful information in supporting table tennis practice. Our method uses a combination of LSTM (Long short-term memory) with a deep state space model and probabilistic inference. More precisely, we use the expressive power of LSTM when handling high-dimensional time series data, and state space model and probabilistic inference to extract low-dimensional latent representations useful for coaching. Experimental results show that our method can yield promising results for characterizing high-dimensional time series patterns and for providing useful information when working with wearable IMU (Inertial measurement unit) sensors for table tennis coaching.

scholarly journals Implicit–explicit gradient of nondual awareness or consciousness as such

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Zoran Josipovic
Keyword(s):  
State Space ◽  
State Space Model ◽  
Neural Correlates ◽  
Scientific Understanding ◽  
The Other ◽  
Two Dimensional ◽  
Global State ◽  
Phenomenal Content ◽  
Space Model ◽  
Dimensional State Space

Abstract Consciousness is multi-dimensional but is most often portrayed with a two-dimensional (2D) map that has global levels or states on one axis and phenomenal contents on the other. On this map, awareness is conflated either with general alertness or with phenomenal content. This contributes to ongoing difficulties in the scientific understanding of consciousness. Previously, I have proposed that consciousness as such or nondual awareness—a basic non-conceptual, non-propositional awareness in itself free of subject-object fragmentation—is a unique kind that cannot be adequately specified by this 2D map of states and contents. Here, I propose an implicit–explicit gradient of nondual awareness to be added as the z-axis to the existing 2D map of consciousness. This gradient informs about the degree to which nondual awareness is manifest in any experience, independent of the specifics of global state or local content. Alternatively, within the multi-dimensional state space model of consciousness, nondual awareness can be specified by several vectors, each representing one of its properties. In the first part, I outline nondual awareness or consciousness as such in terms of its phenomenal description, its function and its neural correlates. In the second part, I explore the implicit–explicit gradient of nondual awareness and how including it as an additional axis clarifies certain features of everyday dualistic experiences and is especially relevant for understanding the unitary and nondual experiences accessed via different contemplative methods, mind-altering substances or spontaneously.

scholarly journals Implicit-explicit gradient of nondual awareness or consciousness as such

2021 ◽  
Author(s):  
Zoran Josipovic
Keyword(s):  
State Space ◽  
State Space Model ◽  
Scientific Understanding ◽  
The Other ◽  
Phenomenal Content ◽  
Space Model ◽  
The Third ◽  
Dimensional State Space ◽  
Third Dimension

Consciousness is multi-dimensional but is most often portrayed with a 2-D map that has global levels or states on one axis, and phenomenal contents on the other. On this map, phenomenal content is conflated with awareness itself, which contributes to ongoing difficulties in the scientific understanding of consciousness. Previously (Josipovic 2014, 2019; Josipovic and Miskovic, 2020) I have proposed that consciousness as such, or nondual awareness - a basic non-conceptual, non-propositional awareness in itself free of subject-object fragmentation, is phenomenally, functionally and neurobiologically, a unique kind that cannot be adequately specified by a 2-D map of levels/modes and contents. Here, I propose an implicit-explicit gradient of nondual awareness to be added as the third dimension on z-axis. an axis to the 2D map of consciousness. Alternatively, within the multi-dimensional state space model of consciousness, nondual awareness can be specified by several vectors, each representing one of its properties.I explore how including the implicit-explicit gradient of nondual awareness as an additional axis clarifies certain features of everyday dualistic experiences and is especially relevant for understanding the unitary and nondual experiences accessed via different contemplative methods, mind altering substances, or spontaneously. I discuss the relevance of this for current theories of consciousness.

Sign in / Sign up

Export Citation Format

Share Document