Science Biology Humans sync their blinks and brain waves to a song's beat Our bodies react to music whether we want it to our not. Breakthroughs, discoveries, and DIY tips sent every weekday. A good song can easily get a listener nodding their head or tapping their foot along to the beat. While you may not initially realize you're doing it, those physical responses to music are still conscious decisions that you can stop whenever you want. According to neuroscientists, however, music also has the ability to influence even some of our involuntary movements.

Blinking serves a crucial physiological function, by clearing debris from our eyes and keeping them lubricated. But now, scientists have found it may also have a cognitive role. In 1945, Arthur Hall at the University of Sheffield in the UK reported on the frequency of blinking as people read aloud, finding that it mostly coincided with gaps in the print. He suggested that blinking may help people take pauses as they read. We may finally know how cognitive reserve protects against Alzheimer's

Despite significant progress in talking head synthesis since the introduction of Neural Radiance Fields (NeRF), visual artifacts and high training costs persist as major obstacles to large-scale commercial adoption. We propose that identifying and establishing fine-grained and generalizable correspondences between driving signals and generated results can simultaneously resolve both problems. Here we present LokiTalk, a novel framework designed to enhance NeRF-based talking heads with lifelike facial dynamics and improved training efficiency. To achieve fine-grained correspondences, we introduce Region-Specific Deformation Fields, which decompose the overall portrait motion into lip movements, eye blinking, head pose, and torso movements. By hierarchically modeling the driving signals and their associated regions through two cascaded deformation fields, we significantly improve dynamic accuracy and minimize synthetic artifacts. Furthermore, we propose ID-Aware Knowledge Transfer, a plug-and-play module that learns generalizable dynamic and static correspondences from multi-identity videos, while simultaneously extracting ID-specific dynamic and static features to refine the depiction of individual characters. Comprehensive evaluations demonstrate that LokiTalk delivers superior high-fidelity results and training efficiency compared to previous methods. The code will be released upon acceptance.

It's not just your face that can be convincingly replicated by a deepfake. It's also your voice -- quite easily as journalist Chloe Beltman found: Given the complexities of speech synthesis, it's quite a shock to find out just how easy it is to order one up. For a basic conversational build, all a customer has to do is record themselves saying a bunch of scripted lines for roughly an hour. "We extract 10 to 15 minutes of net recordings for a basic build," says Speech Morphing founder and CEO Fathy Yassa. The hundreds of phrases I record so that Speech Morphing can build my digital voice double seem very random: "Here the explosion of mirth drowned him out."

Ildar Rakhmatulin* - Ph.D. Electronic Researcher Sebastian Völkl - Brain-Computer-Interface Developer Abstract This paper presents Open-source software and a developed shield board for the Raspberry Pi family of single-board computers that can be used to read EEG signals. We have described the mechanism for reading EEG signals and decomposing them into a Fourier series and provided examples of controlling LEDs and a toy robot by blinking. Finally, we discussed the prospects of the brain-computer interface for the near future and considered various methods for controlling external mechanical objects using real-time EEG signals. License - GNU General Public License v3.0 Keywords: PIEEG, hackerbci, RaspberryPi, EEG, brain-computer interface Abbreviation BCI Brain-computer interface EEG Electroencephalogram SBC Single-board computer ADC Analog-digital converter Introduction When the term BCI is mentioned, many people immediately associate with controlling objects using the power of thought. Now, neuroscience in non-invasive EEG measurement is only getting there. Still, each step brings us closer to that goal and inspires a new generation of scientists and engineers to contribute to this field of science.

How many times do you see a video of famous personalities saying something'strange' which you believe they have would not say? How many times you have seen some false information spread from a trusted source? Are we going to see the movie Face/Off starring Nicolas Cage & John Travolta becoming a reality? Welcome to the era of fake news/fake videos or in technical terms "Deepfake" and It's the term you are going to hear more often in the near future. Deepfakes have garnered widespread attention for their uses in celebrity pornographic videos, revenge porn, fake news, hoaxes, and financial fraud.

It won't be an overstatement to assert that today's digital world almost solely relies on computers and cyber technologies in nearly every public sphere- from simple one-to-one communication to large-scale broadcasting to high-security message transmission. With exponentially escalating relevance of these technologies, rises the importance of cybersecurity. And in recent times, deepfakes pose one of the biggest threats to cybersecurity. What do we really mean by'Deepfakes'? A deepfake is a fraudulent piece of content, which may include videos or audio recordings, that has been manipulated or created using Artificial Intelligence (AI).

Continuous assessment of task difficulty and mental workload is essential in improving the usability and accessibility of interactive systems. Eye tracking data has often been investigated to achieve this ability, with reports on the limited role of standard blink metrics. Here, we propose a new approach to the analysis of eye-blink responses for automated estimation of task difficulty. The core module is a time-frequency representation of eye-blink, which aims to capture the richness of information reflected on blinking. In our first study, we show that this method significantly improves the sensitivity to task difficulty. We then demonstrate how to form a framework where the represented patterns are analyzed with multi-dimensional Long Short-Term Memory recurrent neural networks for their non-linear mapping onto difficulty-related parameters. This framework outperformed other methods that used hand-engineered features. This approach works with any built-in camera, without requiring specialized devices. We conclude by discussing how Rethinking Eye-blink can benefit real-world applications.

A new robotic contact lens which is controlled by small eye movements, including double blinks to zoom in and out, has been created by scientists. The contact lens, which is made from just salt water, works by mimicking the natural electric signals in the human eyeball. There is a steady electrical potential between the eyeball's front and back, even when your eyes are closed or in total darkness. When you move your eyes to look around or blink, the motion of the electrical potential can be measured. Researchers from the University of California, San Diego, developed the lens using these signals, called electro-oculograms, to control a soft lens.

Blink twice to zoom in. A new soft lens can be controlled by your eye movements, pivoting left and right as you look around and zooming in and out when you blink. The human eyeball is electric – there is a steady electrical potential between its front and back, even when your eyes are closed or in total darkness. When you move your eyes to look around or blink, the motion of the electrical potential can be measured.