By capturing gaze points and eye movement patterns, eye tracking technology enables researchers to gain insights into visual attention, cognitive processes, and human behavior. Raw eye tracking data is inherently noisy, containing coordinates with occasional invalid entries. Researchers commonly face the challenge of cleaning and refining this data before proceeding to calculate essential eye tracking metrics. Critical preprocessing steps involve filtering out noise, detecting events such as fixations and saccades, and deriving metrics such as fixation duration, saccade amplitude, and more. This paper introduces a tool that provides users with the ability to preprocess data from both Gazepoint and FOVIO eye trackers. The methodology adopted for this line of work is based on Nyström and Holmqvist's (2010) data and velocity-driven algorithm. Preliminary results show the methodology developed as part of this line of work is promising in terms of improved accuracy and reliability in detecting fixations and saccades. Furthermore, this code and its versatile Graphical User Interface (GUI) has been successfully employed in multiple studies. Finally, we present supplementary code for calculating various eye tracking metrics and percent gaze overlap, extending the utility of our tool for in-depth analysis of visual attention and collaborative gaze behavior.

In the era of advancing neurotechnology, the emergence of brain-brain interfaces (BBIs) has opened up new frontiers in human communication and connectivity. BBIs are direct communication pathways between the brain of one subject and the brain of another subject that allow the users to extract and exchange information. Compared to traditional biomedical devices, brain-brain interfaces were originally more invasive between only two people; however, emerging research paves the way for new non-invasive interfaces between two or more brains. As this technology continues to grow with no current regulatory framework and cognitive connections between individuals become a tangible reality, a crucial question arises: What are the ethical implications of this remarkable technology? In this paper, we embark on a journey of ethical reflections, delving into the intricate considerations and moral dilemmas surrounding BBIs. We examine the fundamental values at stake, such as autonomy, privacy, and the potential for misuse, while drawing insights from established ethical frameworks. Analyzing the risks of this technology presents similar results, where we observe risks of safety from invasive neurosurgery, in addition to privacy-related risks upon the misuse of such an information network. Through a comprehensive analysis, we seek to shed light on the complex interplay between cognitive connections and ethical responsibilities, paving the way for informed decision-making and responsible development of this groundbreaking field.  Since the maliciousness of this interface highly depends on its uses, we conclude that its uses should be currently restricted to the medical field, where it is needed the most. We also provide additional recommendations and future work aiming to pave the way to referenceable standards and frameworks that prevent the exploitation of BBI users and protect their privacy.