All official entries must be received no later than 12:00 GMT(+8) on the July 15, 2024. In the interest of fairness to all participants, late entries will not be accepted or scored. To be eligible you must do all of thefollowing:

1. Register; you should include the username, email address and team name.
2. Submit at least one entry that can be scored during Phase I until the defined deadline
   (12:00 GMT(+8), May 31, 2024).
3. Submit an abstract (up to 1 page) describing your approach on the Scientific Challenge to ICBHI’24 no later than the June 15, 2024.
4. You will be notified if your abstract has been accepted by email from ICBHI’24 until August 01, 2024. Submit afull paper describing your work for the Scientific Challenge following the
   ICBHI’24 rules no later than September 01, 2024 using the regular submissionsystem. Includethe overall score for at least one Phase I or Phase II entry in your paper.
5. Attend ICBHI’24 (Oct.30 ~ Nov. 02, 2024) and present your work there (at least one team member must attend ICBHI).

Please do not submit an analysis of this year's Scientific Challenge data to other conferences or journals until after ICBHI’24 has taken place, so the competitors are able to discuss the results in a single
forum.

All accepted abstracts can be submitted as full text scientific papers within the specified deadline for publication under a Topical Collection of the IUPESM journal Health and Technology. Authors are requested to make sure that all data and materials as well as software application or custom code support their published claims and comply with field standards. Authors are required to make materials, data, code, and associated protocols available to readers (an open link such as git repository must be provided). The full-text article will be subject of peer-review process and subsequent publication in Health and Technology Journal, Issue 14, Volume 6, 2024.

Figure1. Illustration of a single trial of the fMRI task used for data acquisition

For convenience, we already segmented each trial into 25-second segments of relevant data (Figure 2):

• The last 5 seconds of the fixation cross
• The 15 seconds of the video
• The first 5 seconds after the end of the video (to account for the hemodynamic delay)

Furthermore, the fMRI data was converted to a 2D data format using the Brainnetome (BN) anatomical atlas containing 246 regions (Fan et al., 2016). For each region of the atlas, the mean BOLD signal was extracted.
 

Thus, for each trial, a 246 x 25 matrix of fMRI data is available (regions x time samples). fMRI data (BOLD signal) was
recorded at a rate of 1 sample per second. Concurrently, for each video, PPG and respiratory physiological data will be available for the same 25 seconds. The physiological data was collected at 400Hz. Each trial has two labels associated. The first label (CLASS) will be used for classification purposes and contains three classes: -1, 0, and 1, corresponding to negative valence, neutral valence and positive valence, respectively. The second label (LEVEL) corresponds to the valence level reported by the participants (value between -4 and 4).

Figure2. Visualization of the data segmentationperformed.

Train set:

The train set consists of the data from 16 participants. The true labels for both CLASS and LEVEL are available to train

the models.

Test set:

The test set consists of the data from the remaining 4 participants, without the true labels.

Each participant folder is organized with the following contents:

■ fMRI_data.npz numpy compress file:

after load can be access like a dictionary using the key = “data”. Will return a numpy array with shape [30, 246, 25]

■ ppg_data.npz numpy compress file:

after load can be access like a dictionary using the key = “data”. Will return a numpy array with shape [30, 1, 10000]

■ resp_data.npz numpy compress file:

after load can be access like a dictionary using the key = “data”. Will return a numpy array with shape [30,1, 10000]

■ labels.npz numpy compress file: 

afterload can be access like a dictionary using the key = “data”. Will return a　numpy array with shape [30, 2]. The columns will correspond to [CLASS, LEVEL]　labels. The labels.npz file is only present on the subjects of the training
set.

Note:　For both the ppg_data and resp_data there might be incomplete trial data containing missing values, represented by np.nan values.

In addition to the data from the 20 participants, an example of each class of the videos shown to the participants is included in the supplementary folder, as well as the labeling of each region of the BN atlas. In “BN_atlas.txt” file, the first column indicates the index where the region is located in the 2D fMRI data matrix and the second column indicates the name of the region:

Ex: “BN_atlas.txt”

0 A8m_L (first element of the matrix　corresponds to the A8m_L region of the BN atlas)

1 A8m_R

…

245 lPFtha_R

Furthermore, we provide a simple Python　script with sample code for loading the data of a subject, and the　submission.csv file is a template file to be filled and submitted for each　attempt in the challenge.

Supplementary\

　　High_Valence.mp4

　　Low_Valence.mp4

　　Neutral_Valence.mp4

　　BN_atlas.txt

Train\

　　P01\

　　　　　fMRI_data.npz

　　　　　ppg_data.npz

　　　　　resp_data.npz

　　　　　labels.npz

　　P02\

　　　　　…

　　…

　　P16\

　　　　　…

Test\

　　P17\

　　　　　fMRI_data.npz

　　　　　ppg_data.npz

　　　　　resp_data.npz

　　P18\

　　　　　…

　　…

　　P20\

　　　　　…

References:
Di Crosta, A. etal. (2020) "The Chieti Affective Action Videosdatabase, a resource for the study of emotions in psychology", Scientific Data, 7(1), p. 32. doi: 10.1038/s41597-020-0366-1.

Fan, L. et al. (2016) "The HumanBrainnetome Atlas: A New Brain Atlas Based on Connectional Architecture", Cerebral Cortex, 26(8), pp. 3508–3526. doi: 10.1093/cercor/bhw157.