Prediction of Human Performance Using Electroencephalography under Different Indoor Room Temperatures.

Varying indoor environmental conditions is known to affect office worker’s performance; wherein past research studies have reported the effects of unfavorable indoor temperature and air quality causing sick building syndrome (SBS) among office workers. Thus, investigating factors that can predict performance in changing indoor environments have become a highly important research topic bearing significant impact in our society. While past research studies have attempted to determine predictors for performance, they do not provide satisfactory prediction ability. Therefore, in this preliminary study, we attempt to predict performance during office-work tasks triggered by different indoor room temperatures (22.2 °C and 30 °C) from human brain signals recorded using electroencephalography (EEG). Seven participants were recruited, from whom EEG, skin temperature, heart rate and thermal survey questionnaires were collected. Regression analyses were carried out to investigate the effectiveness of using EEG power spectral densities (PSD) as predictors of performance. Our results indicate EEG PSDs as predictors provide the highest R² (> 0.70), that is 17 times higher than using other physiological signals as predictors and is more robust. Finally, the paper provides insight on the selected predictors based on brain activity patterns for low- and high-performance levels under different indoor-temperatures.

Prediction of temperature induced office worker's performance during typing task using EEG.

Understanding how indoor environment affects office worker's performance and developing methods to predict human performance in changing indoor environment have become highly important research topic bearing significant economic and sociological impact. While past research groups have attempted to find predictors for performance they do not provide satisfactory predictions. We conduct in this paper a study to predict human performance by developing a regression model using neurophysiological signals collected from electroencephalogram (EEG), during simulated office-work tasks under different indoor room temperatures (22°C and 30°C). We found that using brain power spectral densities (PSD) from EEG as predictors provides the higher R2 than predictors using skin temperature or heart rate by approximately over 3 folds. Finally, we showed that the predictor using EEG is more robust than regression models using skin temperature and heart rate. Our work shows the potential of using brain signals to accurately predict human office work performance.

Detection of control or idle state with a likelihood ratio test in asynchronous SSVEP-based brain-computer interface systems.

We consider the detection of the control or idle state in an asynchronous Steady-state visually evoked potential (SSVEP)-based brain computer interface system. We propose a likelihood ratio test using Canonical Correlation Analysis (CCA) scores calculated from the EEG measurements. The test exploits the state-specific distributions of CCA scores. The algorithm was tested on offline measurements from 42 participants and the results should a significant improvement in detection error rate over the support vector machine classifier. The proposed test is also shown to be robust against training sample size.

Cooperative control of UAVs for localization of intermittently emitting mobile targets.

Compared with a single platform, cooperative autonomous unmanned aerial vehicles (UAVs) offer efficiency and robustness in performing complex tasks. Focusing on ground mobile targets that intermittently emit radio frequency signals, this paper presents a decentralized control architecture for multiple UAVs, equipped only with rudimentary sensors, to search, detect, and locate targets over large areas. The proposed architecture has in its core a decision logic which governs the state of operation for each UAV based on sensor readings and communicated data. To support the findings, extensive simulation results are presented, focusing primarily on two success measures that the UAVs seek to minimize: overall time to search for a group of targets and the final target localization error achieved. The results of the simulations have provided support for hardware flight tests.

Using parallel evolutionary development for a biologically-inspired computer vision system for mobile robots.

We describe a new approach to attacking the problem of robust computer vision for mobile robots. The overall strategy is to mimic the biological evolution of animal vision systems. Our basic imaging sensor is based upon the eye of the common house fly, Musca domestica. The computational algorithms are a mix of traditional image processing, subspace techniques, and multilayer neural networks.