Electromagnetic waves and mental synchronization of humans in a large crowd

Fiala, Pavel; Hanzelka, Michael; Cap, M.

doi:10.23919/measurement.2017.7983581

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…Because he cannot understand why he gets confronted, he is not interested in cooperation but only in the Super-one who rules him spiritually (Le Bon, 1895). No matter how far away he is, he is always vigilant to ensure that his Super-one, the Ruler he boasts, is not disturbed or does not leave but remains permanent and untouchable (Freud, 1921;Stäheli, 2011;Drury, 2014;Fiala et al, 2017;Maeng & Tanner, 2013;Zeitz et al, 2009). Hatred always pinches him to conspire with kindred souls to fall maliciously over "his equals", plunder and abuse them, and deceive them on demand from the Super-one.…”

Section: The Cause Of the Crowdmentioning

confidence: 99%

“…Collective behavior differs from individual behavior, and both depend on the setting. At the same time, they both affect each other, and both have the seals of culture and epoch (Freud, 1921;Vicsek, 2003;Chiang et al, 2009;Zeitz et al, 2009;Maeng & Tanner, 2013;Drury, 2014;Swathi et al, 2017;Fiala et al, 2017;Templeton et al, 2018;Drury & Reicher, 2020;Bellomo et al, 2022;Raineri, 2022;Campbell, 2022). The Crowd is a feature of humankind's history because humans are naturally gregarious entities who continually seek to be socialized (Templeton et al, 2018;Raineri, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…The Crowd is a feature of humankind's history because humans are naturally gregarious entities who continually seek to be socialized (Templeton et al, 2018;Raineri, 2022). Its awareness and recognition of its capacity to deliberately exploit others constitute the other side of the coin (Drury, 2014;Swathi et al, 2017;Fiala et al, 2017;Raineri, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Crowd Mentality Conveys No-Oneness

Lolja¹

2022

IJSSHR

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The paper inspects the individual's personality concerning self, Crowd Ruler, members, and surrounding setting. It creatively examines the cause and conditions of why individuals make a phycological crowd around a Ruler. The larger the Crowd, the more the individual vanishes within and outwardly. The individual in the Crowd alters himself into a narcissist and, simultaneously, a sentinel toward others. His ego is above everything, so he wants to be a Ruler or the Ruler of the Crowd. On the outside, the gravitational force of collective psychosis and the fear of Crowd's breath convert him into an emotional and malevolent no-one. He loses vision in the Crowd, and his rationality turns off while instantaneous fury turns on. Purpose: This discourse aims at a society led by rationality where individuals live in harmony and peace. Methodology: The duality Crowd-Ruler is reviewed through experience while emotional processes in Crowd members and the Ruler are examined for the accounted social acts or behaviours. Findings: The notion of duality Crowd-Ruler is a sensitive matter because if leaders and decision-makers have this duality like a beacon, society suffers devastation. In addition, the mentality of duality Crowd-Ruler might come across through uncomfortable behaviour in everyday life and work situations. A simple tool to avoid conversion to a No-one is by not thinking, speaking, or writing with "We". Originality: The paper examines first the cause of Crowd making and then outlines the effects of the Crowd eventually on the identity of its members which is the No-oneness

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Section: The Cause Of the Crowdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Crowd Mentality Conveys No-Oneness

Lolja¹

2022

IJSSHR

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“…Therefore, an EEG reading r at time step t can be represented with a single n-dimension vector r t = r 1 t , r 2 t , ..., r n t , where the r i t is the reading of ith electrode sensor at time step t. The EEG record of a single subject in an experiment run is R i t , where i ∈ is the sequence electrode sensor attached on the wearable headset, and t ∈ T is the duration of an experiment run T . tice, each frequency range has uniquely biological significance and signals are well correlated with specific brain functions [6,24], as shown in Table 1. Therefore, an EEG signal can be quantized according to its pathological characteristics in the frequency range from 0.5Hz to 28Hz [24].…”

Section: Data Acquisitionmentioning

confidence: 99%

“…Therefore, EEG-based intention recognition has been widely studied in recent years and become one of the important research topics in the community of pattern recognition [12,20,18,14]. Many promising applications that recognize human intentions via EEG signals, such as EEG signal controlled wheelchairs [6], brain typing [31], and brain wavelet-controlled exoskeleton [7], restore the ability to those individuals who are currently suffering from high degrees of motor disability or locked-in syndrome.…”

Section: Introductionmentioning

confidence: 99%

EEG-based Motion Intention Recognition via Multi-task RNNs

Chen¹,

Wang²,

Zhang³

et al. 2018

Proceedings of the 2018 SIAM International Conference on Data Mining

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Recognition of human intention based on Electroencephalography (EEG) signals attracts strong research interest in pattern recognition because of its promising applications that enable non-muscular communications and controls. Over the past few years, most EEG-based recognition works make significant efforts to learn extracted features to explore specific patterns between a segment of EEG signals and the corresponding activities. Unfortunately, vectorization-based feature representations, either vector-like or matrix-like ones, suffer from massive signal noise and difficulties of exploiting signal correlations between adjacent sensors of EEG signals. Most importantly, EEG signals are represented by one unique frequency and then fed into the subsequent learning model. Neglecting different frequencies of EEG signals can be detrimental to activity recognition because a particular frequency of EEG signals is more helpful to recognize some activities. Inspired by this idea, we propose to extract EEG signals with different frequencies and introduce a novel Multi-task deep learning model to learn the human intentions. We have conducted extensive experiments on a publicly available EEG benchmark dataset and compared our method with many state-of-the-art algorithms. The experimental results demonstrate that the proposed Multi-task deep recurrent neural network outperforms all the compared methods in a multi-class scenario.

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DAMTRNN: A Delta Attention-Based Multi-task RNN for Intention Recognition

Chen

Yue

et al. 2019

Lecture Notes in Computer Science

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Electromagnetic waves and mental synchronization of humans in a large crowd

Cited by 5 publications

References 6 publications

Crowd Mentality Conveys No-Oneness

Crowd Mentality Conveys No-Oneness

EEG-based Motion Intention Recognition via Multi-task RNNs

DAMTRNN: A Delta Attention-Based Multi-task RNN for Intention Recognition

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