Myriad Heavens: Rise of the Rune God

Chapter 95: Chapter 95: Neural Interface

Orion started the training.

Normally this would take days. Maybe weeks. AI training was notoriously slow, even on good hardware. The math was just computationally expensive.

But Nexus and Aether OS changed the equation.

His custom language utilized every transistor in the system. Every CPU core running at maximum efficiency. Every memory bank accessed perfectly. No wasted cycles. No idle resources.

The training completed in three hours.

Orion stared at the screen. ”What.”

He checked the results. Rene had learned pattern recognition. Adaptive resource management. Predictive optimization. Everything he’d designed it to do.

Three hours instead of three months.

He integrated Rene into Aether OS.

A soft chime came from his laptop speakers. Text appeared on screen in a clean, minimalist interface:

INITIALIZATION COMPLETE. I AM RENE, YOUR PERSONAL ASSISTANT. WHAT CAN I DO FOR YOU, MASTER?

Orion raised an eyebrow at the ”master” designation. ”Just call me Orion.”

UNDERSTOOD, ORION. AWAITING INSTRUCTIONS.

”Go into standby mode. I have a hardware project to complete first. Once the BCI is assembled, I’ll need you for neural interface training.”

STANDBY MODE ACTIVATED. I’LL BE READY WHEN YOU NEED ME.

The interface minimized to a small icon in the corner of his screen. Simple. Unobtrusive. Exactly how he wanted it.

Orion leaned back and stretched. Time to wait for the parts to arrive.

The AI would run constantly in the background, invisible to the user. Watching how the computer was used. Learning patterns. Optimizing performance automatically.

But Rene wasn’t finished yet. The AI needed more capabilities. Much more.

Orion pulled up the library in his mind. Searched for neural interface algorithms.

Found what he needed: brain signal processing frameworks.

Raw EEG data was messy. Incredibly messy. When you measured electrical activity on the scalp, you got everything—intentional thoughts, background noise, involuntary processes, muscle movements, even your heartbeat.

Walking generated noise. Blinking generated noise. Breathing, swallowing, jaw clenching—all created electrical signals that the sensors would pick up.

The challenge was separating signal from noise. Finding the patterns that meant ”I want to do this” versus ”my body is just doing normal body things.”

This required sophisticated filtering algorithms.

First layer: frequency analysis. Different brain activities happened at different frequencies. Intentional thought patterns typically showed up in the 8-30 Hz range—alpha and beta waves. Muscle artifacts were higher frequency. Heartbeat was lower frequency.

He could use Fourier transforms to decompose the raw signal into frequency components. Filter out everything outside the useful range.

Second layer: spatial filtering. With six sensors positioned around the ear, he could use the differences between them to isolate brain signals. Brain activity created distinct voltage patterns across multiple sensors. Noise from muscles or eyes affected sensors differently.

Independent Component Analysis—ICA—would separate the mixed signals into independent sources. Brain activity would show up as one component. Eye blinks as another. Jaw movements as another. He could keep the brain component and discard the rest.

Third layer: pattern recognition. This is where machine learning came in. He needed to train an AI to recognize which brain patterns corresponded to specific thoughts.

But not just any AI. He needed one specialized for neural interfaces.

Orion started coding.

He built a neural network architecture specifically designed for EEG analysis. Multiple layers of processing, each learning different aspects of brain signals.

The first layers learned low-level features—basic wave shapes, frequency patterns, voltage relationships between sensors.

Middle layers learned higher-level features—how those basic patterns combined into meaningful signals. What intentional thought looked like versus random background activity.

Final layers learned semantic meaning—translating specific brain patterns into specific commands. ”This pattern means open file. That pattern means type the letter A. This other pattern means execute function.”

But the really clever part was the temporal processing.

Thoughts didn’t happen instantaneously. They built up over time. When you decided to type a word, your brain showed preparatory activity before the actual motor command. The AI needed to recognize those temporal sequences.

He implemented a recurrent neural network—RNN—that could track patterns over time. It would see the build-up of brain activity and predict what command was coming before it fully formed.

This would reduce latency dramatically. By the time you consciously realized you wanted to do something, the AI would already be executing it.

Then came the output translation layer.

Brain patterns needed to map to three types of output: text, images, and video.

For text, he built a language model. The AI would learn which brain patterns corresponded to letters, words, and sentences. It would understand context—the same brain pattern might mean different things depending on what you were doing.

For images, he built a visual encoder. The AI would learn to translate visual imagery in your mind into actual image data. If you imagined a red circle, the AI would generate a red circle on screen.

For video, he extended the visual encoder to handle temporal sequences. Multiple images in succession, capturing motion and change over time.

The whole system was massive. Dozens of interconnected neural networks, each specialized for different aspects of the problem.

He coded for six hours straight. His fingers flying across the keyboard at inhuman speed. The breathing technique kept his mind sharp, his body fresh.

Finally, he had it. A complete neural interface framework.

But it was useless without data.

He needed to train it on actual brain signals. His brain signals specifically. Everyone’s neural patterns were unique—an AI trained on someone else’s brain wouldn’t work for him.

Which meant he needed to build the hardware first.

Orion saved his work and stood up. Stretched. It was time to assemble the BCI.

And just in time the doorbell rang downstairs.

Orion went down to find the apartment empty. A note on the kitchen table in his mom’s handwriting:

Had to go to work early. Breakfast and lunch are in the containers on the counter. Heat them up when you’re hungry. Love you. - Mom

He grabbed the food containers and set them aside. The doorbell rang again—delivery drone waiting for confirmation.

He opened the door. Three packages sat on the doorstep, already scanned and verified by the building’s security system. The delivery drone hovered nearby, waiting for him to confirm receipt.

Orion grabbed all three boxes and dismissed the drone. It beeped once and flew away.

He carried the packages upstairs to his room along with the food his mom had left. Set the food on his desk for later. The packages went right next to them.

Time to build.

The cEEGrid bundle. Inside was a headset with thin electrodes designed to wrap around the ear. Research equipment, bulky and obvious.

He grabbed his precision tools and started disassembling.

The sensors were tiny. Delicate silver electrodes attached to thin copper wiring. Each one designed to detect microvolt-level electrical signals through skin contact.

EEG worked by measuring voltage differences between electrodes. Neurons firing created electrical currents. Those currents flowed through brain tissue—which was mostly saltwater and conducted electricity well. The currents reached the skull, which didn’t conduct as well, causing voltage changes on the skin surface.

The sensors detected those changes. Amplified them. Sent them to processing equipment.

Simple physics. Just incredibly precise.

Orion extracted each sensor carefully. Set them aside in organized rows.

Next box: bone-conducting earbud components.

Bone conduction was weird technology. Instead of pushing sound waves into your ear canal like normal headphones, these vibrated against your skull. The vibrations traveled through bone directly to your cochlea—the hearing organ deep in your ear.

It worked because bone conducted sound vibrations efficiently. You could hear clearly while your ear canal stayed completely open.

This gave Orion two advantages. First, room to hide the EEG sensors where they’d touch skin but wouldn’t interfere with the speakers. Second, the vibration drivers could work in reverse—they’d act as audio output for Rene’s voice responses.

He could think commands to Rene. Rene could respond with audio only he could hear.

Perfect for maintaining privacy.

He cracked open the earbud casing. Studied the internal layout. The vibration driver took up most of the space. But there were gaps. Areas he could use.

His plan: integrate the EEG sensors into the earbud housing. Position them to make skin contact around the ear—above it, behind it, below it. Multiple sensors for better signal quality.

Wire everything to a miniature transmitter. The transmitter would encode the EEG data and broadcast it wirelessly to the smartwatch.

Power would come from micro-batteries. Lithium polymer cells small enough to fit in the earbud but powerful enough to run the sensors and transmitter.

He started assembling.

First, he modified the earbud casing. Used his precision knife to create channels for wiring. Made mounting points for the EEG sensors.

Then he positioned the sensors. Three per earbud—one above the ear, one behind, one on the earlobe. Six total between both earbuds. More sensors meant better signal quality. More data to work with.

He soldered connections. His enhanced vision let him see microscopic details. His steady hands worked at a scale most people would need a microscope for.