Thermal sensation and electroencephalogram (EEG)

Transcription

1 Thermal sensation and electroencephalogram (EEG) Minjung Kim 1, Yoorim Choi 1, Chungyoon Chun 1,* 1 Yonsei University, Seoul, South Korea * Corresponding chun@yonsei.ac.kr SUMMARY This research aimed to provide a fundamental study of using electroencephalograms (EEGs) to reflect the thermal comfort of occupants. We analyzed the occupants' response through the entire EEG spectrum under a wide range of thermal conditions (predicted mean vote (PMV) index: -3 to +3).Seven male undergraduate students participated seven times, respectively, for all conditions of the experiment, which investigated the EEGs of the occupants for 65 minutes under constant thermal conditions in a chamber. We determined that each cognitive process required respective thermal conditions, and the alpha wave associated with relaxation did not coincide with the frequencies of thermal comfort. If the EEG frequency and topographic map that can reflect thermal comfort are revealed through further studies, these would contribute to analyzing the occupants' thermal comfort through the EEG changes and regulate the indoor conditions in tandem with the era of the Internet of Things. PRACTICAL IMPLICATIONS This research investigated the total frequency range of EEG data according to thermal conditions to reveal the brain waves associated with thermal comfort. Through this research, we recognized that measuring EEG can analyze both thermal sensation and the occupants psychological status associated with the cognitive process; based on these results, we can suggest the thermal comfort as well as the best thermal environment taking the occupants' emotions, productivity, and health into consideration in the era of the Internet of Things. KEYWORDS Thermal Environment, Thermal Comfort, Brain Wave 1 INTRODUCTION Thermal sensation is determined through the evaluation of the thermal environment accompanied by cognition process and physiological response. In other words, various factors affect thermal sensation in addition to physical environmental factors, such as changes in emotion, thermal history, or the expectation of the space. Therefore, thermal sensation cannot be comprehended as passive result of the body from thermoreceptors of the physical environmental factors because is a psychophysiological response through the cognitive process via thermo-sensitive neurons. This study is designed to investigate the EEG changes responding to thermal sensation as a cognitive process. Recently, brain science has been adopted actively as a basic instrument for evaluating and predicting individuals' cognition, thoughts, emotions, and so on. It is expected that the technologies associated with brain science, which have been eagerly adopted in various fields, will soon be merged with the regulation systems of indoor environments and control systems. Many studies have focused on indoor environments using brain science. Most of those studies have still dealt with a limited range of frequency. Some researchers have analyzed stress or attention under diverse environmental conditions (Choi et al., 2015; Kim et al., 2014; Lee et

2 al., 2012a; Lee et al., 2009) or have researched only alpha waves under the hypothesis that indoor comfort was considered to correlate with relaxation (Choi, 2011; Im et al., 2015; Kim et al., 1998; Kim et al., 2006; Kum et al., 2007; Lee et al., 2012b). Among them, Yao et al. (2008; 2009) measured EEG based on thermal sensation vote (TSV) responses under diverse thermal conditions, and they analyzed the entire frequency range. Nonetheless, they selected a relatively moderate indoor temperature range and subjected the participants to four conditions (21, 24, 26, 29 ), in turn, in one day, which could have caused the order effect. Kim and Kim (2004) invented the algorithm for evaluating comfort using EEG data. Although they conducted research regarding thermal comfort to confirm the efficacy of the algorithm, the reliability was low for thermal comfort. We provide fundamental data for exploring EEG results related to the thermal sensation of occupants based on these studies. Thus, we investigated the occupants' EEG responses over a total range of frequency under a wide scope of thermal conditions (predicated mean vote (PMV) index: -3 to +3). It is expected that this study will contribute to discovering the candidate frequency range for thermal comfort. 2 METHODS 2.1 Experimental Conditions This study was conducted in a climate chamber, which can control temperature, humidity, and air speed, at Yonsei University, Seoul, South Korea. The following physical conditions of the environment were regulated: air temperature ( ), radiant temperature ( ), relative humidity (%), and air velocity (m/s). The participants personal environments clothing insulation (clo) and metabolic rate (met) were calculated based on the ISO Relative humidity was set to 50%, air velocity was 0.1 m/s, clothing was 0.8 clo, and metabolic rate was 1.0 met. Air temperature was controlled by each PMV level, as shown in Table 1. Because the climate chamber was located inside of a building, solar radiation rarely affected the chamber. Thus, it was speculated that the radiant temperature was in accordance with the air temperature. To maintain the consistency of the thermal equilibrium status of each participant, the condition of the pre-conditioning chamber was set to 24.4 and 50% equivalent to a neutral thermal sensation (PMV 0). Table 1. Climate chamber conditions and related PMV values. PMV Air Temperature ( ) Set Actual ± ± ± ± ± ± ± Measuring Tools: The EEG Recording System The eight EEG channels of the bio-signal instrumentation system MP 150 (Biopac Systems Inc., Santa Barbara, CA, USA) were applied to measure the EEG. A monopolar montage with the reference site on the earlobe was adopted, and the EEG data were collected from eight points, according to the International system. The eight sites of EEG measurement were selected according to the location, function, and folds of the brain as follows: frontal lobe (Fp1, Fp2, F3, F4), temporal lobe (T3, T4), and parietal lobe (P3, P4). The occipital lobe,

3 including the visual cortex, was excluded because visual conditions were out of the research scope of this study. EEGs were recorded using Ag/AgCl cap style electrodes (model: CAP100C). The sampling rate was 1000 Hz, and a bandpass filter of 0.1 Hz to 35 Hz was used. Eye movement by blinking was measured by Ag/AgCl disposable electrodes (Tyco Healthcare Group LP, Norwalk, CT, USA), and the artifacts from this eye movement were removed in the EEG analysis. The EEG data were collected for 65 minutes, consecutively, and the data from the last 30 seconds were used for the analysis. As there have been few previous studies on EEG measurements of thermal sensation to establish a standard time slot for analysis, in this present study the time slot was constructed using the visual inspection method based on the EEG data of the total period. The collected EEG data were analyzed according to frequency using spectral analysis. The target frequencies were chosen as follows: relative δ (0 Hz to 4 Hz/0 Hz to 50 Hz), relative θ (4 Hz to 8 Hz/0 Hz to 50 Hz), relative α (8 Hz to 13 Hz/0 Hz to 50 Hz), relative β (13 Hz to 30 Hz/0 Hz to 50 Hz), and relative γ (30 Hz to 50 Hz/0 Hz to 50 Hz). The differences among each of the EEG wave s based on TSV responses were statistically analyzed using one-way ANOVA and Tukey's post-hoc analysis using the statistical package, SPSS Experimental Procedures The experiment was approved by the Institutional Review Board (IRB) at Yonsei University and it was conducted from September to December Seven male undergraduate students (ages: 22 28) participated in the study and received monetary compensation. To exclude the order effects, each participant experienced seven environmental conditions in respective sequence, and a total of 49 experiments were performed. The participants wore prepared experimental clothes (0.8 clo) and waited for 20 minutes in the pre-conditioning chamber set at a neutral temperature. They then moved to the climate chamber after the EEG electrodes were attached, after which they participated in the experiment by engaging in general studying or reading activities for 65 minutes. At the end of the experiment, they provided their TSV responses based on the ASHRAE seven-point scale. 3 RESULTS Figure 1 shows the results of the frequencies that were analyzed by spectral analysis of the EEG data from eight channels. (mv) 0.90 relative δ (mv) 0.25 relative θ Cold Cool 0.00 Cold Cool

4 (mv) 0.25 relative α (mv) 0.25 relative β Cold Cool (mv) relative γ Cold Cool Cold Cool Figure 1. EEG results according to the TSV responses. In one-way ANOVA analysis, it was revealed that the EEG changes according to the TSV responses were significant for all of the frequency ranges (relative δ wave: F= 5.32, p <.001; relative θ wave: F= 7.16, p <.001; relative α wave: F= 3.72, p <.01; relative β wave: F= 2.67, p <.05; relative γ wave: F= 2.42, p <.05). For a more accurate analysis, the Tukey s post hoc test results are shown in Table 2. The differences in the relative between the "hot" and "cold" responses were significant in all of the frequencies, and most of the differences of relative between the other TSV responses and "hot" were also found to be significant. Although there were significant differences between most of the TSV responses and "hot" in the relative δ waves, only the difference between "hot" and "cold" was significant in the relative β waves. Moreover, there were significant differences in the largest number of pairs in the relative θ waves. Table 2. Tukey s post-hoc test results. Cool Cool Cold *** Cool ** Relative δ * * Cold * *** Cool * Relative * *** θ *

5 Relative α Relative β Relative γ Cold ** Cool * * Cold ** Cool Cold * Cool * * p <.05, ** p <.01, *** p < DISCUSSION The criteria classifying the frequency band differ among previously published studies (Klimesch, 1999), and each frequency band has continuous values with overlapping areas. Thus, the frequency bands that were close were interpreted to have similar functions. Furthermore, as brain waves are the sum of oscillations from diverse parts of the brain, depending on various brain functions, the analyses have to consider the various frequencies simultaneously (Bazanova and Vernon, 2014). The alpha band, which is the band that is most well-known and widely studied, has been known to reflect the resting state (Choi, 2011; Im et al., 2015; Kim et al., 1998; Kim et al., 2006; Kum et al., 2007; Lee et al., 2012b); however, it has also been interpreted in other ways based on its relationship with other frequencies (Jaušovec and Jaušovec, 2005; Klimesch, 1999). For example, one study concluded that the reduction of EEG θ waves that occurred with the increase of EEG α waves was the result of decreased attentional or semantic memory demands (Klimesch, 1999), and another study determined that δ β coherence was a reflection of the emotion regulation process (Putman, 2011). Thus, we analyzed the meaning of the frequencies in the integrative view under TSV responses rather than separating the range of frequencies. There have been diverse studies focusing on the association between EEG δ waves and sleep because sleep state is related to blood flow, which affects EEG δ waves (Davis et al., 2011). Specifically, higher δ amplitude is evoked, not in the deep sleep state, but in the awake state just before falling asleep. Davis et al. (2011) reported that EEG δ waves with high amplitude are one of the characteristics of REM sleep, and they used them as an index of the sleep stage because these waves had a significant correlation with the duration and intensity of sleep in various situations. Although EEG θ waves were also mentioned as causes of sleep deprivation or cognitive processes (Klimesch, 1999; Schacther, 1977), the patterns of the relative θ in this study were different from those of the relative δ, thereby implying the sleep stage. Thus, in the present study, EEG θ waves were analyzed based on the participant s cognitive process. The cognitive process has been found to be influenced by the working memory system and the long-term memory system (Klimesch, 2013). In particular, EEG θ waves have been found to be correlated with episodic encoding, which implies the process of capturing contextual information in the working memory system (Hsieh and

6 Ranganath, 2014; Klimesch, 1999). EEG θ waves have been found to show a contrary movement to α waves, and the frequencies of these two waves were found to be correlated with attentional demand (Klimesch, 1999; Ray and Cole, 1985). High attentional demand induced an increase in θ, whereas low attentional demand caused an increase in α. Thus, the resting state, which implies low attentional demand, was mentioned frequently as a feature of EEG α waves (Choi, 2011; Im et al., 2015; Kim et al., 1998; Kim et al., 2006; Kum et al., 2007; Lee et al., 2012b). As EEG β waves have been found to reflect alertness, the increase in β suggested a rapid response to the stimulus (Noachtar et al., 1999; Kamiński et al., 2012). β waves have been shown to have task-relevant properties as well (Ray and Cole, 1985). EEG γ waves reflected various cognitive processes, such as spatial selective attention, recognition according to perception, memory, and language processing. In particular, the γ has been found to increase when cognitive processing at a higher level is needed (Jaušovec and Jaušovec, 2005; Miltner et al., 1999; Müller et al., 2000). Based on the information presented above, the results of this present study could be interpreted as follows. The relative δ was highest in the "hot" condition and lowest in the "cold" condition. The of the other frequencies (θ, α, β, and γ), all of which, with the exception of the δ band, reflect the cognitive process, was significantly lower in the participants whose TSV response was hot than it was in those who had other TSV responses. Thus, although the participants with the "hot" sensation were awake, they were in a drowsy cognitive state and showed a low level of cognitive processing. The participants reporting a "cold" sensation could not feel any sleepiness because the coldness induced a high level of arousal. These results were in accordance with the findings from a previous study that showed that most occupants on a floor with a high temperature were in a drowsy cognitive state (Lee et al., 2012b). Meanwhile, the relative θ and α showed aspects that were contrary to each other in the "," "slightly," and "neutral" ranges. It was speculated that the attentional demand of the participants who felt "slightly " was high and the participants with "neutral" sensation showed a relaxed state with relatively low attentional demand. The changes of relative α and relative β were opposed to each other in the "neutral," "slightly," and "" ranges. This implies that the participants with a "slightly " TSV response showed increased alertness according to higher attentional demand. Although the relative θ, β, and γ s also represented cognitive processes, they have respective, specific functions. Therefore, the highest EEG of θ appeared in the "slightly " state, which was different from that of the β and γ s, showing the highest in the "slightly " state. These results were in accordance with the findings from previous studies in that the best temperature condition for attention differed for each type of attention (Kim et al., 2014; Lee et al., 2012a). The relative β showed minimal variance based on the TSV response except for the "hot" sensation; this could be explained by the task-relevant feature of EEG β waves (Ray and Cole, 1985). The relative γ also showed minimal change and a relatively low value in contrast to the other frequencies, which could be explained by the absence of a highlevel cognitive process. 5 CONCLUSIONS We investigated the characteristics of occupants' EEGs under various thermal conditions as a fundamental way to determine the EEG of thermal comfort. We recognized that contextual interpretations are essential for analyzing EEG data rather than focusing on a specific EEG frequency range.

7 This study s findings revealed that when participants felt extreme thermal sensations, such as "hot" and "cold," they could barely perform cognitive activities. The findings also showed that each cognitive process changed in varying degrees according to the subjective thermal sensations, except for the "hot" and "cold" conditions; thus, it could be speculated that each thermal sensation should be suggested for each type of required cognitive process. It could also be inferred that the hypothesis that thermal comfort is regarded as relaxation, which has been posited in previous studies, was incorrect. Based on the results mentioned above, EEG frequency and topographical mapping which can effectively represent thermal comfort need to be investigated further in future studies. ACKNOWLEDGEMENT This research was supported by Basic Science Research Program through the National Resear ch Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2013R1A1A ). 6 REFERENCES Bazanova O.M. and Vernon D Interpreting EEG alpha activity. Neuroscience & Biobehavioral Reviews, 44, Choi S.S The effects of a color environment on the emotional evaluation of space. Journal of Korea Digital Design Council, 11(4), Choi Y.R., Kim M.J., and Chun C.Y Measurement of occupants' stress based on electroencephalograms (EEG) in twelve combined environments. Building and Environment, 88, Colgin L.L Mechanisms and functions of theta rhythms. Annual Review of Neuroscience, 36, Davis C.J., Clinton J.M., Jewett K.A., Zielinski M.R., and Krueger J.M EEG delta wave : An independent sleep phenotype or epiphenomenon? Journal of Clinical Sleep Medicine, 7(5), Hsieh L.T., Ekstrom A.D., and Ranganath C Neural oscillations associated with item and temporal order maintenance in working memory. The Journal of Neuroscience, 31(30), Hsieh L.T. and Ranganath C Frontal midline theta oscillations during working memory maintenance and episodic encoding and retrieval. Neuroimage, 85, Im G.H., Kim J.H., Park. C.S., and Cho H.H An experimental study of the bioelectrical signals and subjective response in changing from unpleasant to pleasant temperatures in a learning environment. Korean Journal of Air-Conditioning and Refrigeration Engineering, 27(11), Jaušovec N. and Jaušovec K Differences in induced gamma and upper alpha oscillations in the human brain related to verbal/performance and emotional intelligence. International Journal of Psychophysiology, 56(3), Kamiński J., Brzezicka A., Gola M., and Wróbel, A Beta band oscillations engagement in human alertness process. International Journal of Psychophysiology, 85(1), Kim C.J., Chung S.I., Jeong H.J., Kim Y.J., Han S.H., Cho Y.W., Park J.I., Hong H.G., Min B.I., and Kim K.H Thermal comfort and physiological change under artificial environment in winter. The Korean Journal of Stress Research, 6(1), 1-8. Kim D.J. and Kim H.H Comfortableness evaluation method using EEGs of the frontopolar and the parietal lobes. The Transactions of the Korean Institute of Electrical Engineers, 53(5),

8 Kim H.C., Kum J.S., Shin B.H., and Chung Y.H Research in physiology signal change of thermal-comfort evaluation by air conditioner temperature change. Journal of Fisheries and Marine Sciences Education, 18(1), Kim M.J., Choi Y.R., Han J.E., Son Y.J., and Chun C.Y An experiment on attention ability based on electroencephalogram (EEG) in different PMV conditions. Proceeding of the Windsor Conference. Klimesch W EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Research Reviews, 29(2), Klimesch, W. (2013). The Structure of Long-term Memory: A Connectivity Model of Semantic Processing. Psychology Press. Kum J.S., Kim D.G., and Kim H.C A study of physiology signal change by air conditioner temperature change. Journal of Fisheries and Marine Sciences Education, 19(3), Lee H.J., Choi Y.R., and Chung C.Y. 2012a. Effect of indoor air temperature on the occupants attention ability based on the electroencephalogram analysis. Journal of the Architectural Institute of Korea, 28(3), Lee H.J., Choi Y.R., and Chung C.Y. 2012b. The effect of floor surface temperature on occupant s relaxation. Journal of the Korean Society of Living Environmental System, 19(4), Lee J.S., Jeong J.H., Kim J.H., Lee K.J., Song H.M., Jeon B.J., and Lee J.Y The effects of implementation of tinted eyeglasses on concentration through EEG examination. The Journal of the Korean Society of Occupational Therapy, 17(2), Miltner W.H., Braun C., Arnold M., Witte H., and Taub E Coherence of gamma-band EEG activity as a basis for associative learning. Nature, 397(6718), Müller M.M., Gruber T., and Keil A Modulation of induced gamma band activity in the human EEG by attention and visual information processing. International Journal of Psychophysiology, 38(3), Noachtar S., Binnie C., Ebersole J., Mauguiere F., Sakamoto A., and Westmoreland B A glossary of terms most commonly used by clinical electroencephalographers and proposal for the report form for the EEG findings. The International Federation of Clinical Neurophysiology. Electroencephalography and Clinical Neurophysiology. Suppl (52), Putman P Resting state EEG delta beta coherence in relation to anxiety, behavioral inhibition, and selective attentional processing of threatening stimuli. International Journal of Psychophysiology, 80(1), Ray W.J. and Cole H.W EEG alpha activity reflects attentional demands, and beta activity reflects emotional and cognitive processes. Science, 228(4700), Roberts B.M., Hsieh, L.T., and Ranganath C Oscillatory activity during maintenance of spatial and temporal information in working memory. Neuropsychologia, 51(2), Schacter D.L EEG theta waves and psychological phenomena: A review and analysis. Biological Psychology, 5(1), Yao Y., Lian Z., Liu W., and Shen Q Experimental study on physiological responses and thermal comfort under various ambient temperatures. Physiology & Behavior, 93(1), Yao Y., Lian Z., Liu W., Jiang C., Liu Y., and Lu H Heart rate variation and electroencephalograph The potential physiological factors for thermal comfort study. Indoor Air, 19(2),