This work signifies a pivotal step in EEG research by presenting a dataset that authentically captures EEG signals in naturalistic, real-world settings, moving beyond the traditional laboratory environments. By utilizing the P300/RSVP task in this dataset, we aim to differentiate the EEG results obtained in the presence of noise from those obtained in noise-free conditions. This differentiation allows for a comprehensive analysis of the impact of noise on EEG signals and facilitates the evaluation of signal-denoising techniques. The P300/RSVP task is particularly relevant as it involves measuring accuracy, making it an effective dependent variable to assess the efficacy of noise cleaning and evaluate the influence of behavioral artifacts on the EEG signals. Furthermore, we can gain insights into the relationship between noise, behavioral artifacts, and the quality of EEG signals, ultimately enhancing our understanding of the robustness of EEG data in real-world settings. For the purpose of creating this dataset, participants were instructed to produce particular artifacts at particular times via a carefully controlled protocol, e.g., moving head left to right vs. up and down, eye movement, eye blinks, facial expressions, lip movement, body movement, etc. The specific artifacts that participants were instructed to produce during data recording reflect the most problematic artifacts encountered in real-world EEG recording. This initiative opens new horizons for the exploration of brain-computer interfaces and EEG signal analysis, equipping researchers with a rich resource to develop and validate novel methodologies, ultimately advancing the field of neuroscience and human-computer interaction.