This paper introduces a novel asynchronous adaptive brain machine interface (BMI), based on a dry-wireless headset, to trigger the movement of a lower limb exoskeleton robot by foot motor imagery. Specifically, it addresses two issues that are critical for the development of a plug-and-play brain robot interface (BRI): setup-time and the nonstationarity of the electroencephalogram (EEG). The former is solved by a dry-wireless headset that reduces setup-time compared to gel-based systems, and removes the nuisance of cables. The latter has been extensively studied in the literature, leading to effective adaptive algorithms in synchronous BMI. However, asynchronous BMI has received little attention. We propose an extension of state-of-the-art adaptive methods by defining the forgetting factors according to the time constant of the exponential moving average. In addition, we propose feature adaptation as opposed to the standard bias adaptation of a linear classifier. After calibrating the decoder, the subject with a reliable classification of sensorimotor rhythms was asked to trigger robot squatting. The motion was successfully initialized by foot motor imagery; with an essential contribution of the proposed adaptive BMI, which makes features less prone to nonstationarities and improves classification performance compared to standard adaptive methods. The ultimate goal of our research is to develop a plug-and-play co-adaptive BRI for neuromotor rehabilitation.