Several motor related Brain Computer Interfaces (BCIs) have been developed over the years that use activity decoded from the contralateral hemisphere to operate devices. Many recent studies have also talked about the importance of ipsilateral activity in planning of motor movements. For successful upper limb BCIs, it is important to decode finger movements from brain activity. This study uses ipsilateral cortical signals from humans (using ECoG) to decode finger movements. We demonstrate, for the first time, successful finger movement detection using machine learning algorithms. Our results show high decoding accuracies in all cases which are always above chance. We also show that significant accuracies can be achieved with the use of only a fraction of all the features recorded and that these core features also make sense physiologically. The results of this study have a great potential in the emerging world of motor neuroprosthetics and other BCIs.