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\appendix
\section{\texttt{LOC} dataset}\label{appendix:loc}
\paragraph{$g$-forces and fighter pilots.}
Earth's gravity, commonly referred to as $1 g$, induces a constant acceleration. Fighter pilots, however, encounter much higher accelerations, reaching up to $9 g$ along the z-axis. This force induces a blood shift towards the lower body, reducing blood perfusion in the brain. Without mitigation, pilots can experience altered consciousness, encompassing diminished peripheral vision, central vision loss, Almost Loss Of Consciousness (ALOC), and G-Induced Loss of Consciousness (GLOC)~\cite{morrissette2000further}, sometimes with fatal outcomes.
Due to the eye's heightened sensitivity to hypoxia, initial symptoms are often visual. As retinal blood pressure drops below Intraocular pressure (usually 10–21 mm Hg), blood flow diminishes, impacting the retina initially in areas farthest from the optic disc and central retinal artery before progressing toward central vision. Fighter pilots train in centrifuges\footnote{
Equipment capable of reproducing the intensity and jolt of the $g$-forces accelerations experienced in high-performance fighter jet.} to apprehend the effects of $+g_z$ accelerations and perform the Anti-$g$ Straining Maneuvers (AGSM). AGSM consist of muscle contractions in the lower limbs, synchronized with breathing exercices. These maneuvers aim to elevate arterial blood pressure to sustain blood volume in the brain~\cite{pollock2021oh}. 
Each training is monitored by doctors through physiological data acquired in real time: electrocardiograms (ECG), electromyograms of the quadriceps (EMG), the acoustic breathing (AUDIO), the pressure on the pedals (PAL), the visual field (VF)\footnote{Self-filled in by pilots}, and through video monitoring.
Several factors~\cite{pollock2021oh,park2015unpredictability,nunneley1979heat}, may influence pilots' tolerance to $g$-forces, thus posing challenges for doctors in detecting alterations of consciousness, both in centrifuge simulations and in-flight. Moreover, expanding detection to real scenarios is not trivial. The modalities differ: the video and visual field data are impractical due to pilots' equipment (helmets and full-face $O_2$ masks) and technical constraints, respectively.

\paragraph{Case study with medical doctors.} 
\input{figures/case-study-figure}
Given the rare availability of videos, we conducted a case study with a sample of doctors in charge of the aeromedical training. The study aimed to assess the efficacy of relying solely on biomedical signals for identifying loss of consciousness. 

\noindent \emph{Protocol.} Each doctor was presented with 20 complete launches accompanied by biomedical signals and tasked with labeling them as "Loss of consciousness" (Example in Figure~\ref{fig:gloc-ex}) or "No loss of consciousness" (Example in Figure~\ref{fig:no-gloc-ex}). 

\noindent \emph{Results.} Surprisingly, doctors accurately classified 55\% of launches with a variance of 6.53\% using only biomedical signals. However, when provided with videos, doctors achieved 100\% accuracy in launch classification. This underscores the crucial role of video data and emphasizes the necessity, from a medical standpoint, to incorporate its robust representation.



\paragraph{Positive and negative samples.}
To detect \textit{alteration of consciousness}, we train our models for binary classification (distinguishing between \textit{altered} and \textit{unaltered}) 
on $n$-seconds windows. Such a window $w_t {=} [t {-} n, t]$ is considered positive if there is some consciousness alteration period $(t_s, t_e)$ such that $t {-} n {\geq} t_s$ and  $t {\leq} t_e$. Otherwise, $w_t$ is considered negative. We fix $n=3.1sec$.



