Zapping the brain has allowed partially paralysed patients to walk again in a'major milestone' for wheelchair users. Deep brain stimulation has been found to improve walking and promote recovery in two people with a spinal cord injury. The surgical procedure involves implanting electrodes into the brain to produce electrical impulses. These can be easily switched'on' and'off'. Traditionally, it has been used to treat movement disorders like Parkinson's by targeting areas of the brain responsible for motor control.

A new device has been created that can analyse the brainwaves of non-verbal, paralysed patients and turn them into sentences on a computer screen in real time. The'mind-reading' machine is capable of decoding brain activity as a person silently attempts to spell out words phonetically to create full sentences. Experts say their neuroprosthesis speech device has the potential to restore communication to people who cannot speak or type due to paralysis. Previous research had shown that a similar system was able to decode up to 50 words. However, this was limited to a specific vocabulary and the participant had to attempt to speak the words out loud, which required significant effort, given their paralysis.

For more than a decade, neuroscientist Grégoire Courtine has been flying every few months from his lab at the Swiss Federal Institute of Technology in Lausanne to another lab in Beijing, China, where he conducts research on monkeys with the aim of treating spinal-cord injuries. The commute is exhausting -- on occasion he has even flown to Beijing, done experiments, and returned the same night. But it is worth it, says Courtine, because working with monkeys in China is less burdened by regulation than it is in Europe and the United States. And this week, he and his team report the results of experiments in Beijing, in which a wireless brain implant -- that stimulates electrodes in the leg by recreating signals recorded from the brain -- has enabled monkeys with spinal-cord injuries to walk. "They have demonstrated that the animals can regain not only coordinated but also weight-bearing function, which is important for locomotion. This is great work," says Gaurav Sharma, a neuroscientist who has worked on restoring arm movement in paralysed patients, at the non-profit research organization Battelle Memorial Institute in Columbus, Ohio.

Motivated by the particular problems involved in communicating with "locked-in" paralysed patients, we aim to develop a braincomputer interface that uses auditory stimuli. We describe a paradigm that allows a user to make a binary decision by focusing attention on one of two concurrent auditory stimulus sequences. Using Support Vector Machine classification and Recursive Channel Elimination on the independent components of averaged eventrelated potentials, we show that an untrained user's EEG data can be classified with an encouragingly high level of accuracy. This suggests that it is possible for users to modulate EEG signals in a single trial by the conscious direction of attention, well enough to be useful in BCI.

Motivated by the particular problems involved in communicating with "locked-in" paralysed patients, we aim to develop a braincomputer interface that uses auditory stimuli. We describe a paradigm that allows a user to make a binary decision by focusing attention on one of two concurrent auditory stimulus sequences. Using Support Vector Machine classification and Recursive Channel Elimination on the independent components of averaged eventrelated potentials, we show that an untrained user's EEG data can be classified with an encouragingly high level of accuracy. This suggests that it is possible for users to modulate EEG signals in a single trial by the conscious direction of attention, well enough to be useful in BCI.

Motivated by the particular problems involved in communicating with "locked-in" paralysed patients, we aim to develop a braincomputer interfacethat uses auditory stimuli. We describe a paradigm that allows a user to make a binary decision by focusing attention on one of two concurrent auditory stimulus sequences. Using Support Vector Machine classification and Recursive Channel Eliminationon the independent components of averaged eventrelated potentials,we show that an untrained user's EEG data can be classified with an encouragingly high level of accuracy. This suggests that it is possible for users to modulate EEG signals in a single trial by the conscious direction of attention, well enough to be useful in BCI.