A technique based on five brain rhythms ( \delta , \theta , \alpha , \beta , and \gamma ) presented in the sequence for analyzing Electroencephalography (EEG) signals has been proposed. First, the production of the sequence has been accomplished by selecting the prominent brain rhythm having the maximum instantaneous power at specific timestamp consecutively throughout the EEG. To this purpose, the reassigned smoothed pseudo Wigner-Ville distribution (RSPWVD) has been employed. Then, in order to verify the proposed technique and evaluate its performance, a case study of seizure detection has been implemented. As experimental validation, 93 patients from the Karunya database have been investigated. Moreover, to characterize the brain rhythm sequence for seizure detection, two additional indices derived from the power discharge and synchronous behavior have been applied. Results show that the particular rhythm pattern during the seizure is usually one type (either \delta , \theta , or \alpha ) and it is subject-dependent. Hence, by focusing on the changes of such particular rhythm through the two indices, the time-related occurrences of seizures can be determined in detail. Meanwhile, the representative channels for seizure detection can be found by studying the similarity of sequences, which are helpful to reduce the number of applied channels. Finally, the proposed technique provides an accuracy of 98.9%, which demonstrates it is competent to detect the appearances of abnormal seizures from the EEG signals reliably. Consequently, the brain rhythm sequencing could open a new way to interpret and characterize the EEG in various applications such as for epileptic patients.