Student Leaders Today, Professional Leaders Tomorrow.

One of the core missions of the IEEE Engineering in Medicine and Biology Society (EMBS) is to be a platform for enhancing the personal and professional development of its members. This month we focus on two related priority areas of the IEEE EMBS Student Activities Committee (SAC) [1], namely Leadership Development and Professional Development Portfolios, and bring you up close to the student and professional leaders actively building these programs. The Leadership Development Portfolio, currently led by Agnieszka Lach from Silesian University of Technology, Gliwice, Poland, focuses on nurturing and supporting student leaders of the EMBS globally. The Professional Development Portfolio, currently led by Josée Rosset from the University of Manitoba, Winnipeg, MB, Canada, aims to help EMBS student members develop their skills and experiences in the practice of biomedical engineering.

Interested in Volunteering for EMBS? Here Are Two Students' Exciting Experiences.

The IEEE Engineering in Medicine and Biology Society (EMBS) is the world's largest international society of biomedical engineers. Volunteering for IEEE EMBS can be an enriching experience for students. We recently interviewed two exceptional EMBS volunteers to gain insight on their volunteering experience with EMBS.

Crafting the Future-Opportunities for Students by the Reinvigorated IEEE EMBS Student Activities Committee.

"As we look ahead into the next century, leaders will be those who empower others."-Bill Gates.

NeuroVAD: Real-Time Voice Activity Detection from Non-Invasive Neuromagnetic Signals.

Neural speech decoding-driven brain-computer interface (BCI) or speech-BCI is a novel paradigm for exploring communication restoration for locked-in (fully paralyzed but aware) patients. Speech-BCIs aim to map a direct transformation from neural signals to text or speech, which has the potential for a higher communication rate than the current BCIs. Although recent progress has demonstrated the potential of speech-BCIs from either invasive or non-invasive neural signals, the majority of the systems developed so far still assume knowing the onset and offset of the speech utterances within the continuous neural recordings. This lack of real-time voice/speech activity detection (VAD) is a current obstacle for future applications of neural speech decoding wherein BCI users can have a continuous conversation with other speakers. To address this issue, in this study, we attempted to automatically detect the voice/speech activity directly from the neural signals recorded using magnetoencephalography (MEG). First, we classified the whole segments of pre-speech, speech, and post-speech in the neural signals using a support vector machine (SVM). Second, for continuous prediction, we used a long short-term memory-recurrent neural network (LSTM-RNN) to efficiently decode the voice activity at each time point via its sequential pattern-learning mechanism. Experimental results demonstrated the possibility of real-time VAD directly from the non-invasive neural signals with about 88% accuracy.