Sub-micron pupillometry for optical EEG measurements

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Niels-Ole Rohweder ◽  
Jan Gertheiss ◽  
Christian Rembe
Keyword(s):  
Noise Source ◽  
Pupil Diameter ◽  
Brain Activity ◽  
Frequency Range ◽  
Noise Sources ◽  
Human Machine Interaction ◽  
Pixel Resolution ◽  
Small Oscillations ◽  
Frequency Space ◽  
Machine Interaction

Abstract Recent research indicates that a direct correlation exists between brain activity and oscillations of the pupil. A publication by Park and Whang shows measurements of excitations in the frequency range below 1 Hz. A similar correlation for frequencies between 1 Hz and 40 Hz has not yet been clarified. In order to evaluate small oscillations, a pupillometer with a spatial resolution of 1 µm is required, exceeding the specifications of existing systems. In this paper, we present a setup able to measure with such a resolution. We consider noise sources, and identify the quantisation noise due to finite pixel sizes as the fundamental noise source. We present a model to describe the quantisation noise, and show that our algorithm to measure the pupil diameter achieves a sub-pixel resolution of about half a pixel of the image or 12 µm. We further consider the processing gains from transforming the diameter time series into frequency space, and subsequently show that we can achieve a sub-micron resolution when measuring pupil oscillations, surpassing established pupillometry systems. This setup could allow for the development of a functional optical, fully-remote electroencephalograph (EEG). Such a device could be a valuable sensor in many areas of AI-based human-machine-interaction.

Identifying Human-Machine Interaction Problems in Continuous State Data

2016 ◽  
Vol 60 (1) ◽  
pp. 86-90
Author(s):  
Arik-Quang V. Dao ◽  
James R. Parkinson ◽  
Steven J. Landry
Keyword(s):  
Human Error ◽  
Pupil Diameter ◽  
Control Parameters ◽  
Human Machine Interaction ◽  
Continuous State ◽  
Operator Control ◽  
And Control ◽  
Main Effects ◽  
Machine Interaction ◽  
Aircraft Data

A set of studies has been focused on identifying “markers” in aircraft data that are indicative of human factors issues. In this paper we discuss an experiment that investigated if human error is predictable from the error observed from the combined human-machine system. Sixteen pilots flew simulated instrument approaches under varying levels of workload and control augmentation conditions. Operator control lag, gain, delay, and error extent were computed from aircraft lateral path errors. These parameters along with pupil diameter data were analyzed for differences across workload conditions. Main effects for workload were found with respect to all control parameters consistent with the experiment hypotheses, but the effects were very small. Operator delay in responding to errors appeared inversely correlated with workload. Statistically significant differences were also found with respect to error extent ad pupil diameter.

scholarly journals Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives

2020 ◽  
Vol 7 ◽  
Author(s):  
Matteo Spezialetti ◽  
Giuseppe Placidi ◽  
Silvia Rossi
Keyword(s):  
Emotion Recognition ◽  
Affective Computing ◽  
Brain Activity ◽  
Human Robot Interaction ◽  
Point Of View ◽  
Robot Interaction ◽  
Human Machine Interaction ◽  
Recent Advances ◽  
Critical Issues ◽  
Machine Interaction

A fascinating challenge in the field of human–robot interaction is the possibility to endow robots with emotional intelligence in order to make the interaction more intuitive, genuine, and natural. To achieve this, a critical point is the capability of the robot to infer and interpret human emotions. Emotion recognition has been widely explored in the broader fields of human–machine interaction and affective computing. Here, we report recent advances in emotion recognition, with particular regard to the human–robot interaction context. Our aim is to review the state of the art of currently adopted emotional models, interaction modalities, and classification strategies and offer our point of view on future developments and critical issues. We focus on facial expressions, body poses and kinematics, voice, brain activity, and peripheral physiological responses, also providing a list of available datasets containing data from these modalities.

Quantification of aerodynamically induced noise and vibration-induced noise in a suction unit

2011 ◽  
Vol 225 (3) ◽  
pp. 617-624 ◽  
Author(s):  
J Prezelj ◽  
M Čudina
Keyword(s):  
Transfer Function ◽  
Noise Reduction ◽  
Noise Source ◽  
Operating Conditions ◽  
Frequency Range ◽  
Centrifugal Blower ◽  
Noise Sources ◽  
Noise And Vibration ◽  
Different Types ◽  
The Individual

Noise, generated by a centrifugal blower, can be divided according to its origin, into aerodynamically induced noise and vibration-induced noise. The contribution of the individual noise source to the total emitted noise is hard to determine, but it is crucial for the design of noise reduction measures. In order to reduce the noise of the centrifugal blower in a broad range of operating conditions, an identification of noise sources needs to be performed. An analysis of the most important noise origin in a centrifugal blower presented in this article was performed by measurements of the transfer function between noise and vibration, under different types of excitation. From the analyses one can conclude that the dominant noise source of a centrifugal blower can be attributed to the aerodynamically generated noise which exceeds the vibration-induced noise for more than 10 dB in a broad frequency range.

A Comparison of Beamforming Processing Techniques for Low Frequency Noise Source Identification in Mining Equipment

2009 ◽  
Author(s):  
Hugo E. Camargo ◽  
Patricio A. Ravetta ◽  
Ricardo A. Burdisso ◽  
Adam K. Smith
Keyword(s):  
Source Identification ◽  
Hot Spots ◽  
Noise Source ◽  
Low Frequency ◽  
Adaptive Beamforming ◽  
Frequency Noise ◽  
Mining Equipment ◽  
Frequency Range ◽  
Noise Sources ◽  
Noise Source Identification

In an effort to reduce Noise Induced Hearing Loss (NIHL) in the mining industry, the National Institute for Occupational Safety and Health (NIOSH) is conducting research to develop noise controls for mining equipment whose operators exceed the Permissible Exposure Level (PEL). The process involves three steps: 1) Noise source identification (NSI), 2) development of noise controls, and 3) evaluation of the developed noise controls. For the first and third steps, microphone phased array measurements are typically conducted and data are processed using the conventional beamforming (CB) algorithm. However, due to the size and complexity of the machines, this task is not straight forward. Furthermore, because of the low frequency range of interest, i.e., 200 Hz to 1000 Hz, results obtained using CB may show poor resolution issues which result in inaccuracy in the noise source location. To overcome this resolution issue, two alternative approaches are explored in this paper, namely the CLEAN-SC algorithm and a variarion of an adaptive beamforming algorithm known as Robust Capon Beamformer (RCB). These algorithms were used along with the CB algorithm to process data collected from a horizontal Vibrating Screen (VS) machine used in coal preparation plants. Results with the array in the overhead position showed that despite the use of a large array, i.e., 3.5-meter diameter, the acoustic maps obtained using CB showed “hot spots” that covered various components, i.e., the screen deck, the side walls, the I-beam, the eccentric mechanisms, and the electric motor. Thus, it was not possible to identify which component was the dominant contributor to the sound radiated by the machine. The acoustic maps obtained using the RCB algorithm showed smaller “hot” spots that in general covered only one or two components. Nevertheless, the most dramatic reduction in “hot” spot size was obtained using the CLEAN-SC algorithm. This algorithm yielded acoustic maps with small and well localized “hot” spots that pinpointed dominant noise sources. However, because the CLEAN-SC algorithm yields small and localized “hot” spots, extra care needs to be used when aligning the acoustic maps with the actual pictures of the machine. In conclusion, use of the RCB and the CLEAN-SC algorithms in the low frequency range of interest helped pinpoint dominant noise sources which otherwise would be very hard to identify.

scholarly journals "Thinking out loud": an open-access EEG-based BCI dataset for inner speech recognition

2021 ◽  
Author(s):  
Nicolas Nieto ◽  
Victoria Peterson ◽  
Hugo Leonardo Rufiner ◽  
Juan Kamienkowski ◽  
Ruben Spies
Keyword(s):  
Speech Recognition ◽  
Open Access ◽  
Brain Activity ◽  
Inner Speech ◽  
Human Machine Interaction ◽  
New Techniques ◽  
Inner Voice ◽  
Electrical Brain Activity ◽  
Computer Interfaces ◽  
Machine Interaction

Surface electroencephalography is a standard and noninvasive way to measure electrical brain activity. Recent advances in artificial intelligence led to significant improvements in the automatic detection of brain patterns, allowing increasingly faster, more reliable and accessible Brain-Computer Interfaces. Different paradigms have been used to enable the human-machine interaction and the last few years have broad a mark increase in the interest for interpreting and characterizing the "inner voice" phenomenon. This paradigm, called inner speech, raises the possibility of executing an order just by thinking about it, allowing a "natural" way of controlling external devices. Unfortunately, the lack of publicly available electroencephalography datasets, restricts the development of new techniques for inner speech recognition. A ten-subjects dataset acquired under this and two others related paradigms, obtained with an acquisition system of 136 channels, is presented. The main purpose of this work is to provide the scientific community with an open-access multiclass electroencephalography database of inner speech commands that could be used for better understanding of the related brain mechanisms.

Noise source location and scaling of subsonic upper-surface blowing

2020 ◽  
Vol 19 (3-5) ◽  
pp. 191-206
Author(s):  
Trae L Jennette ◽  
Krish K Ahuja
Keyword(s):  
Strouhal Number ◽  
Noise Source ◽  
Low Frequency ◽  
Directivity Pattern ◽  
Source Location ◽  
Dipole Source ◽  
Frequency Noise ◽  
Noise Sources ◽  
Trailing Edge ◽  
Trailing Edge Noise

This paper deals with the topic of upper surface blowing noise. Using a model-scale rectangular nozzle of an aspect ratio of 10 and a sharp trailing edge, detailed noise contours were acquired with and without a subsonic jet blowing over a flat surface to determine the noise source location as a function of frequency. Additionally, velocity scaling of the upper surface blowing noise was carried out. It was found that the upper surface blowing increases the noise significantly. This is a result of both the trailing edge noise and turbulence downstream of the trailing edge, referred to as wake noise in the paper. It was found that low-frequency noise with a peak Strouhal number of 0.02 originates from the trailing edge whereas the high-frequency noise with the peak in the vicinity of Strouhal number of 0.2 originates near the nozzle exit. Low frequency (low Strouhal number) follows a velocity scaling corresponding to a dipole source where as the high Strouhal numbers as quadrupole sources. The culmination of these two effects is a cardioid-shaped directivity pattern. On the shielded side, the most dominant noise sources were at the trailing edge and in the near wake. The trailing edge mounting geometry also created anomalous acoustic diffraction indicating that not only is the geometry of the edge itself important, but also all geometry near the trailing edge.

Concise CNN model for face expression recognition

2021 ◽  
pp. 1-9
Author(s):  
Harshadkumar B. Prajapati ◽  
Ankit S. Vyas ◽  
Vipul K. Dabhi
Keyword(s):  
Network Architecture ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Face Expression ◽  
Computational Overhead ◽  
Average Accuracy ◽  
Proposed Model ◽  
Face Expression Recognition ◽  
Expression Classification ◽  
Machine Interaction

Face expression recognition (FER) has gained very much attraction to researchers in the field of computer vision because of its major usefulness in security, robotics, and HMI (Human-Machine Interaction) systems. We propose a CNN (Convolutional Neural Network) architecture to address FER. To show the effectiveness of the proposed model, we evaluate the performance of the model on JAFFE dataset. We derive a concise CNN architecture to address the issue of expression classification. Objective of various experiments is to achieve convincing performance by reducing computational overhead. The proposed CNN model is very compact as compared to other state-of-the-art models. We could achieve highest accuracy of 97.10% and average accuracy of 90.43% for top 10 best runs without any pre-processing methods applied, which justifies the effectiveness of our model. Furthermore, we have also included visualization of CNN layers to observe the learning of CNN.

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