---
title: 'GAMEEMO: Facial & EEG Emotion Datasets'
url: https://www.emergentmind.com/topics/gameemo-dataset
type: topic
---

# GAMEEMO: Facial & EEG Emotion Datasets

The label **GAMEEMO** is used in two distinct senses in the affective computing literature represented here. In the facial-expression literature, **GAMEEMO** is an alias for the **GaMo (game-based emotion) dataset**, introduced in “Towards an ‘In-the-Wild’ Emotion Dataset Using a Game-based Framework” and collected from a web game that automatically labels webcam facial images [1607.02678]. In later EEG-based work, **GAMEEMO** denotes a dataset of **14-channel EEG recordings with continuous self-reported emotion ratings from serious games**, used for subject-independent emotion classification across binary valence, multi-class, and fine-grained multi-label settings [2508.21103]. The shared name therefore designates two different resources, one image-based and one EEG-based, with different annotation schemes, collection protocols, and intended uses.

## 1. Naming, provenance, and scope

The facial-expression resource is officially named **GaMo (game-based emotion) dataset**. In some citations and follow-on work it is also referred to as **GAMEEMO**; in the 2016 paper, however, the authors consistently use **GaMo**, and “GAMEEMO” is explicitly described as an alias for the same dataset. That paper is authored by **Wei Li**, **Farnaz Abtahi**, **Christina Tsangouri**, and **Zhigang Zhu**, with affiliations at the **Department of Electrical Engineering, CUNY City College**, the **Department of Computer Science, CUNY Graduate Center**, and the **Department of Computer Science, CUNY City College**, New York, USA. Data collection was conducted under **IRB approval**, and all users signed consent [1607.02678].

The EEG resource described in the later literature is a different dataset. In “Spatiotemporal EEG-Based Emotion Recognition Using SAM Ratings from Serious Games with Hybrid Deep Learning,” GAMEEMO consists of **14-channel EEG recordings** and **continuous self-reported emotion ratings** from **28 subjects** across **four emotion-inducing gameplay scenarios**. That paper references **GAMEEMO (Alakus et al., 2020, Biomedical Signal Processing and Control, 60:101951)**, but does not provide a direct repository link or full acquisition metadata [2508.21103].

| Usage of the name | Modality | Core annotation scheme |
|---|---|---|
| GaMo / GAMEEMO alias | Webcam facial images | Target emotion shown in the game at capture time |
| GAMEEMO in EEG work | 14-channel EEG | Continuous ratings for boring, horrible, calm, funny |

This dual usage matters because the two datasets are not interchangeable. A plausible implication is that references to “GAMEEMO” should be interpreted only in conjunction with modality, citation, and task definition.

## 2. GaMo as a game-based facial emotion dataset

GaMo was proposed to create an **“in-the-wild” facial emotion dataset** with a **large number of balanced samples**. The motivating problem was that existing facial-emotion datasets were either **constrained**—for example, in laboratory settings with controlled lighting and pose, such as **CK+**, **MMI**, and **DISFA**—or else collected from media or the web, such as **CIFE** and **EmotiW**, but **heavily imbalanced**, especially with over-representation of **happiness** and **sadness** and under-representation of **disgust** and **fear**. The proposed remedy was a **game-based framework** for low-cost crowdsourcing with built-in supervision, class-balance control, and automatic labeling [1607.02678].

The framework has **three components**: a **game engine**, a **game interface**, and a **data collection and evaluation module**. The game engine is a **CNN emotion classifier** trained on **CIFE** and deployed **server-side** because of computational demand. For each input image it returns a **7-way probability vector**. The game interface is a web-based **“Tower Defense”-style** game in which emotion icons, described as **“bombs,”** drop from the top of the screen. The player’s webcam video is displayed; to defuse a bomb and score, the player imitates the displayed emotion, and the engine must recognize the match before the bomb reaches the ground. Each session has **five lives (“hearts”)**, and emotion targets are sampled **with equal probability across the seven emotions** to promote class balance [1607.02678].

Two gameplay modes are described. In the **general version (recognition)**, the browser receives the server’s probability vector $p$ and saves an image if the top prediction matches the current target. In the **customized version (verification)**, the problem is simplified to checking whether the current image expresses the known target emotion, using **per-emotion thresholds** because some emotions are harder to mimic. The decision rule is

$$
\text{accept emotion } e \text{ if } p_e \ge \tau_e,
$$

where $\tau_e$ is a predefined threshold for class $e$. The customized version also allows each registered user to build **seven emotion templates**. The server extracts CNN features for each template and classifies incoming frames by nearest-template comparison using the $L_2$ distance,

$$
d(x, t_k) = \|x - t_k\|_2,
$$

choosing the nearest template as the recognized class. The paper states that this personalization improves user scores and encourages longer play, thereby increasing data volume [1607.02678].

Operationally, images are transmitted at **1 Hz**, both to limit server load and to allow time for expression formation. **Face detection** is performed during template registration; if no face is detected, the user is prompted to recapture. Server inference time is reported as **about 200 ms per image**, which the authors state keeps gameplay smooth [1607.02678].

## 3. Dataset composition, annotation, and empirical behavior of GaMo

GaMo contains **15,455 images**, collected in the first month of operation. The dataset covers seven basic emotion classes: **Angry**, **Disgust**, **Fear**, **Happy**, **Neutral**, **Sad**, and **Surprise**. The per-class counts are **1,945 Angry**, **1,838 Disgust**, **1,586 Fear**, **3,185 Happy**, **2,741 Neutral**, **1,898 Sad**, and **2,262 Surprise**. More than **100 users** played the general version, **74 users** tried the customized version during the first month, and a separate **five-user study**—**3 male, 2 female**—was used for engine evaluation. The exact number of unique contributors to the dataset is not specified, but collection involved **college students** across both versions [1607.02678].

The images are described as **“in-the-wild”** because they were collected with **built-in or external webcams**, under **natural lighting**, with **varied viewing angles and distances**, and in users’ own environments. The resulting images include both **explicit and subtle expressions**, as well as **occasional occlusion** and **non-frontal poses**. Labels are assigned automatically: every saved image inherits the **target emotion shown in the game at capture time**. In the general version, saving requires that the engine’s predicted class match the target; in the customized version, saving requires threshold verification or nearest-template agreement. The authors report that **no manual cleanup was performed** and that random spot checks did not reveal labels **“very off”** from the true expressions [1607.02678].

The CNN used as the game engine is based on the **“Alex” model (AlexNet)**, pretrained on **ImageNet** and fine-tuned on **CIFE** for seven emotions. The architecture is modified so that **fc7 has 2048 kernels** and **fc8 has 7 outputs**, with earlier layers also updated during fine-tuning. After GaMo was collected, a second CNN was trained by further fine-tuning the same backbone on **GaMo** [1607.02678].

Evaluation is reported both as self-test and cross-dataset test. The **CIFE-trained model** achieved **average accuracy 0.74** on CIFE, with per-class accuracies **Angry 0.68**, **Disgust 0.29**, **Fear 0.44**, **Happy 0.87**, **Neutral 0.75**, **Sad 0.79**, and **Surprise 0.73**. The **GaMo-trained model** achieved **average accuracy 0.64** on GaMo, with **Angry 0.65**, **Disgust 0.57**, **Fear 0.52**, **Happy 0.71**, **Neutral 0.71**, **Sad 0.64**, and **Surprise 0.65**. The paper emphasizes that the GaMo result is **more balanced across classes**. In cross-dataset evaluation, the **CIFE-trained model tested on GaMo** dropped to **average 0.21**, with especially low performance on **Angry 0.03**, **Disgust 0.02**, and **Happy 0.03**. By contrast, the **GaMo-trained model tested on CIFE** reached **average 0.50**, with **Happy 0.80** and more moderate transfer across the other classes. The authors interpret this asymmetry as evidence that **GaMo images capture more subtle, realistic expressions**, while **CIFE** often contains **exaggerated expressions**, and that training on GaMo yields a **more robust detector**. A five-user gameplay study further showed **higher average scores with the GaMo engine** than with the CIFE-trained engine [1607.02678].

The paper does not report official **train/validation/test splits**. Recommended use is therefore framed in terms of **fine-tuning CNNs on GaMo** for robust recognition of subtle expressions and using **cross-evaluation against CIFE or similar datasets** to assess generalization [1607.02678].

## 4. GAMEEMO as an EEG dataset derived from serious games

In the EEG literature represented here, GAMEEMO is a dataset of **28 subjects**, each recorded with **14 EEG channels** during **four emotion-inducing gameplay sessions**. Each session is paired with continuous self-reported ratings for four affective states: **boring**, **horrible**, **calm**, and **funny**. The paper states both **“scale 1–10”** and **“continuous scale 0–10”**, and it does not explain the discrepancy. The exact **channel names**, **montage**, **hardware/device**, **sampling rate**, **reference scheme**, **number of trials per session**, **within-session structure**, **duration per scenario**, **total recording time**, and **environment setup** are not provided [2508.21103].

The title references **Self-Assessment Manikin (SAM)**, but the paper does not provide details on the classical **SAM pictorial items** or on any mapping from **valence/arousal/dominance** to the four emotion ratings. Instead, the work uses **direct per-emotion intensity ratings** for **boring**, **horrible**, **calm**, and **funny**. The paper also mentions **“synchronized PDF-based self-reports,”** but the exact timing of rating—whether during or immediately after each scenario—the precise synchronization mechanism, and the rating frequency are not specified [2508.21103].

Three task formulations are defined from these ratings. For **binary valence classification**, **funny** and **calm** are treated as positive, while **boring** and **horrible** are treated as negative. With per-window or per-segment ratings $s_{\text{funny}}$, $s_{\text{calm}}$, $s_{\text{boring}}$, and $s_{\text{horrible}} \in [0,10]$, the paper defines

$$
P = \frac{s_{\text{funny}} + s_{\text{calm}}}{2}, \qquad
N = \frac{s_{\text{boring}} + s_{\text{horrible}}}{2},
$$

and

$$
y_{\text{valence}} =
\begin{cases}
1, & \text{if } P > N,\\
0, & \text{otherwise.}
\end{cases}
$$

For **multi-class emotion classification**, the label is the **most dominant emotion**,

$$
y_{\text{class}} = \arg\max_{e \in \{\text{boring},\text{horrible},\text{calm},\text{funny}\}} s_e.
$$

The paper does not provide a tie-breaking strategy. For **fine-grained multi-label representation**, each emotion rating is binned into ordinal classes. However, the paper states both **“10 ordinal classes”** and **“11 ordinal bins (0–10)”**, without specifying exact bin edges or encoding details [2508.21103].

## 5. Preprocessing, feature extraction, and modeling for EEG GAMEEMO

The EEG pipeline uses **temporal window segmentation** with a sliding window length of **$\tau = 500\,\text{ms}$** and **50% overlap**. **All 14 channels** are used. The paper mentions **padding** to maintain uniform segment lengths, but does not specify whether this is zero-padding, reflection, or another method. It also does not specify **band-pass ranges**, **notch filters**, **artifact removal procedures** such as ICA or EOG regression, or any **re-referencing** step [2508.21103].

Normalization is performed by **z-score normalization**,

$$
z = \frac{x - \mu}{\sigma},
$$

where $x$ is the feature value, $\mu$ is the mean, and $\sigma$ is the standard deviation computed over the training set. The paper presents this generally and does not specify whether the statistics are **per-subject** or **global** [2508.21103].

Feature extraction combines time-domain and frequency-domain descriptors. The paper lists **mean**, **variance**, **standard deviation**, and **entropy** as time-domain features, and also mentions **skewness** and **kurtosis** in the dataset section. Frequency-domain features are based on **FFT-based power spectral density** and **band powers** across **$\delta$**, **$\theta$**, **$\alpha$**, and **$\beta$**, using the generic band-power definition

$$
P_b = \int_{f_1}^{f_2} S(f)\,df,
$$

where $S(f)$ is the spectral density and $[f_1, f_2]$ defines the band. The exact band boundaries are not provided. **Gamma** is mentioned in related work on other datasets, but not used explicitly in the paper’s own feature list [2508.21103].

The evaluated models include the classical baselines **Logistic Regression (LR)**, **Support Vector Machine (SVM)**, **Random Forest (RF)**, and **XGBoost (XGB)**, alongside the deep architectures **LSTM**, **LSTM-GRU**, and **CNN-LSTM**; the algorithm section also writes the latter as **LSTM-CNN**. Architectural details such as the number of layers, hidden units, and kernel sizes are not provided. The stated optimization setup uses **AdamW**, initial learning rate **$\eta = 0.005$**, **cosine annealing** with **warm-up**, **dropout 0.3**, **weight decay $10^{-4}$**, **gradient clipping norm $\le 1.0$**, **100 epochs**, and **batch size 32**. The evaluation uses a **subject-independent 80:20 split**, with subjects in validation or test unseen during training [2508.21103].

The paper states **cross-entropy (CE)** for binary and categorical settings and also states **“Multi-class: BCE”** in Algorithm 1, noting an unconventional pairing that is not further clarified. This inconsistency is part of the reported methodological record rather than a resolved design choice [2508.21103].

## 6. Reported results, limitations, and interpretive issues

For the EEG dataset, the reported best model is **LSTM-GRU**. In **binary valence classification**, it achieves **F1-score = 0.932** and **accuracy = 93.3%**. In **multi-class classification**, its per-class metrics are **boring: F1 0.939, accuracy 0.955, precision 0.942**; **horrible: F1 0.943, accuracy 0.953, precision 0.948**; **calm: F1 0.927, accuracy 0.938, precision 0.929**; and **funny: F1 0.935, accuracy 0.934, precision 0.934**, with **macro average F1 0.936**, **accuracy 0.945**, and **precision 0.938**. In the **fine-grained multi-label** setting, LSTM-GRU reaches **accuracy = 90.6%**, **F1-score = 0.909**, and **precision = 0.909**. The corresponding **Random Forest** results are lower: approximately **85% accuracy** in binary valence, **macro average F1 0.758** and **accuracy 0.796** in multi-class, and **accuracy 0.79**, **F1-score 0.79**, **precision 0.79** in the fine-grained multi-label setting. The paper describes the LSTM-GRU confusion matrices as having a **sharper diagonal** and **fewer off-diagonal errors**, while Random Forest is said to struggle more with overlapping classes and with confusion between **“boring”** and **“horrible”** in the fine-grained setting [2508.21103].

The paper attributes the performance advantage of LSTM-GRU to the ability of a **hybrid temporal architecture** to capture **spatiotemporal dependencies** in the **500 ms overlapping windows**, and to the effect of **feature-rich preprocessing** and **z-score normalization** in reducing **inter-subject variability**. It does not provide **ablation** or **sensitivity analyses**, although it reports training curves with **stable convergence** and **no obvious overfitting** [2508.21103].

Both dataset lines come with explicit limitations. For **GaMo**, the paper notes that class imbalance is **reduced but not eliminated**, that **automatic labeling** can introduce **potential noise**, that the total size of **more than 15,000 images** is still **modest relative to modern deep learning needs**, and that early data were collected primarily from **college students willing to play an online game**, with possible effects from **self-selection** and **hardware variability**. The use of **customized templates** may also encode **user-specific expression styles** [1607.02678]. For the **EEG GAMEEMO** dataset as described in the later work, limitations include the relatively small sample of **28 subjects**, possible noise or bias in **self-reported emotion intensities**, the unresolved **0–10 versus 1–10 scale ambiguity**, the inconsistency between **10** and **11** ordinal bins, and the absence of crucial acquisition details such as **device**, **sampling rate**, **montage**, **reference**, **filtering**, and **artifact removal** [2508.21103].

Taken together, the two usages of **GAMEEMO** correspond to two different methodological traditions within affective computing. The facial-image resource emphasizes **interactive game-based collection**, **balanced class design**, and **automatic labeling** under **in-the-wild** visual conditions. The EEG resource, as used in later modeling work, emphasizes **serious-game induction**, **continuous self-report**, and **multigranularity affective classification** from **14-channel** electrophysiological data. This suggests that the most important prerequisite for correct interpretation is not the shared name itself, but the associated **modality**, **paper citation**, and **label definition**.

Source: https://www.emergentmind.com/topics/gameemo-dataset