Sleep and dreaming are important research topics. Unfortunately, the methods for researching them have several shortcomings. In-laboratory polysomnographic sleep and dream research is a costly, time-consuming and effortful endeavor, often resulting in small subject counts. Moreover, the unfamiliar sleeping environment can lead to distorted measurements as compared to the natural sleep environment at the subject’s home. Conducting sleep and dream experiments in the field by a crowd of subjects could be a solution. However, complex experiment paradigms cannot be investigated this way, because there are no tools available, which enable naive subjects to carry out complex polysomnographic studies on their own. The Traumschreiber system, which is developed and evaluated in this dissertation, offers a solution to this problem. It consists of a high-tech sleep mask and a minicomputer, and enables naive crowd subjects to perform complex polysomnographic sleep and dream experiments at home. On the one hand, it instructs the crowd subject, what to do when. On the other hand, it controls the experiment during the time the subject is asleep, analyzing the data in real-time using state-of-the art machine learning techniques. The rationale behind is to enable a big data approach to sleep and dream research, using the data recorded by a crowd of subjects for large-scale investigations about sleep and dreaming, with low costs for the researcher. After describing the development process of the Traumschreiber system, its usefulness regarding crowd-based automated polysomnographic field studies is evaluated. First, it is validated against a commercial medical poly­somnographic sleep laboratory system, demonstrating its good polysomnographic data recording capabilities – including measurements of EEG, EOG, EMG and ECG –, which enable the researcher to identify typical sleep patterns like slow waves or rapid eye movements as well as sleep stages in the recorded data. Furthermore, two field studies show, that the Traumschreiber system can be used successfully by naive subjects to conduct complex sleep experiments at their homes. This includes acoustic stimulation of the sleeping subject as well as sleep stage dependent activities of the system. The sleep staging algorithm implements a Keras/Tensorflow based neural network approach, which demonstrates the system’s readiness for state-of-the-art machine learning techniques. However, the currently used neural network is kept very simple and can determine the sleep stage not very reliably; it should be further developed and trained on more data of more subjects. The Traumschreiber system will be made available under an open source license, enabling any researcher to use, modify or further develop it. A description, how to produce arbitrarily many entities of the Traumschreiber system, is given in this dissertation and shows that one system can be produced at low costs in a short amount of time. Taken together, the Traumschreiber system is a new tool for sleep and dream research, which enables a crowd-based and machine learning-driven approach to gathering polysomnographic data from complex sleep and dream experiments.