# Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits
## 2 Related Work
### Open Datasets for Affective Computing  
Open datasets are a staple of reproducible and verifiable science and may often catalyze significant research activity. Table 1 presents an overview of publicly available datasets in Affective Computing. We particularly focus on datasets that encompass data originating from physiological sensors such as EEG, electroocologram (EOG), electrocardiogram (ECG), electromyogram (EMG), blood volume pulse (BVP), electrodermal activity (EDA), respiration rate (RESP), eye tracker, magnetoencephalography (MEG), skin temperature (TEMP), acceleration (ACC), beat-to-beat intervals (RR sensor), and pulse oximetry (SpO2) sensors. Six datasets--Ascertain [39], Amigos [40], DEAP [41], Mahnob [42], Decaf-movies, and Decaf-music [43]--are emotion recognition datasets where the participants watched affective multimedia in short sessions, e.g., with a duration of 50 to 80 seconds, and rated their experience after each affective session using psychological questionnaires. In all the datasets, the affective multimedia are short movie or music video clips designed to induce certain affective states (e.g., fear, surprise, joy, etc.). While the participants were watching the affective multimedia, their physiological response was recorded using a variety of devices. The dataset Emotions differs from the previous datasets as it contains data from a single participant over three weeks, standing in contrast to the studies that examine many participants over a short recording interval. Laughter is another slightly different dataset, which aims at laughter recognition using non-invasive wearable devices. Three datasets--Driving-workload [44], Driving-stress [45], and Driving-distractions [46]--are collected in studies where the main task is driving. In Driving-workload, the participants drove a predefined route including different sections (e.g., crowded vs. free highway) and marked their \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Dataset** & **Participants** & **Scenario** & **Signals** \\ \hline Ascertain & 58 & Valence, arousal, liking, engagement, familiarity, Big Five & ECG, EDA, EEG, facial activation units \\ Amigos & 40 & Valence, arousal, control, familiarity, liking and discrete emotions & EEG, ECG, GSR, face video \\ DEAP & 32 & valence, arousal, liking, dominance, and familiarity & ECG, EDA, EEG, EMG, EOG, RESP, TEMP, face video \\ DECAF-music & 30 & Valence, arousal, and dominance & ECG, EMG, MEG, near-infrared face \\ DLECAF-video & Mahnob & 30 & Valence, arousal, dominance, predictability, and discrete emotions & ECG, EDA EEG, RESP, TEMP, face and body video, eye gazetracker, audio \\ Emotions & 1 & Neutral, anger, hate, grief, joy, platonic love, romantic love, reverence & ECG, EDA, EMG, RESP \\ Laughter & 34 & Laughter vs. other & ACC, EDA, PPG, TEMP \\ \hline Driving-work. & 10 & Perceived cognitive load while driving & GSR, HR, TEMP \\ Driving-stress. & 24 & Stress levels (low, medium, high) & ECG, EDA, EMG, RESP \\ Driving-distract. & 64 & Stress binary + NASA TLX & EDA,HR, RESP, facial expressions, eye tracking \\ \hline Stress-math & 21 & Stress levels: (low, medium, high) & ACC, EDA, HR, TEMP, BVP \\ Non-EEG & 20 & Four types of stress (physical, emotional, cognitive, none) & ACC, EDA, HR, TEMP, SpO2 \\ WESAD & 15 & Neutral, amusement, stress & ACC, EDA, TEMP, BVP, EMG, RESP \\ \hline CogLoad & 23 & 6 different cognitive load tasks. Each with three difficulty levels (easy, medium, hard). Additionally, 2-back and 3-back tasks. NASA-TLX & ACC, EDA, TEMP, RR \\ Snake & 23 & Smartphone game with three difficulty levels (easy, medium, hard). NASA-TLX & ACC, EDA, TEMP, RR \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Publicly available datasets from related work. mental workload afterwards by watching a video recording of the driving session. Similarly, in the Driving-stress dataset, the participants drove different sections and marked the perceived stress level.