New insight into how the human motor cortex encodes complex handwriting

Compared to other animal species, humans can plan and execute highly sophisticated motor tasks, including the ability to write complex characters using their hands. While many past studies have tried to better understand the neural underpinnings of handwriting and other complex human motor capabilities, these have not yet been fully elucidated.

Past studies showed that the motor cortex plays a crucial role in the human ability to translate intentions into actions. Yet the processes via which it enables the execution of precise and sequential movements, such as those associated with handwriting, are poorly understood.

Researchers at Zhejiang University in China recently carried out a study aimed at further exploring the role of the human motor cortex in the encoding of intricate handwriting, such as Chinese characters. Their findings, published in Nature Human Behavior, suggest that this encoding unfolds via a sequence of stable neural states.

“When we started this project in 2022, we initially aimed to construct a Chinese handwriting brain-computer interface (BCI) system,” Yu Qi, first author of the paper, told Medical Xpress. “Although our BCI system could decode the movement velocity well for cursor control, the reconstruction of Chinese handwriting was far from satisfactory.

“We were frustrated at the time, because we realized that handwriting is a skilled, sophisticated movement, especially for Chinese characters (> 3,500 frequent characters composed by 32 types of strokes), thus it could be more difficult compared with cursor control.”

Despite their initially disappointing results, Qi and her colleagues continued working on their handwriting BCI, by exploring the processes via which the brain, specifically the motor cortex encodes complex handwriting. Ultimately, their efforts yielded interesting new insight that allowed them to improve their handwriting human-machine interface.

As part of their recent study, they carried out a series of experiments in which a human participant was asked to write 306 distinct Chinese characters. While the participant was completing the task, the researchers recorded activity in his motor cortex, to understand the processes involved in the encoding of the characters and execution of the handwriting task.

“The experiment task was simple,” explained Qi. “We asked our participant to write Chinese characters one by one, with the guidance of a video, just like a karaoke game for writing. We recorded single-unit MC neural activity from his motor cortex (MC) with two microelectrode arrays as he performed the handwriting task.”

The brain activity recordings gathered by the researchers provided new interesting insights into the underpinnings of handwriting, suggesting that the motor cortex encodes complex handwriting by breaking it into a series of small movement segments or states. In their paper, Qi and her colleagues hypothesize that these states are the primitive units of movement encoding.

“The movement encoding in MC showed a state-dependent property that the encoding function remains stable within each state while varying largely across states,” said Qi. “Traditional neural decoders did not consider this state-dependent neural property such that failed to decode handwriting trajectories.

“These findings then inspired us to propose the state-dependent encoding and decoding model, along with the development of computational tools such as TFC and the state-dependent decoder, which enabled online Chinese handwriting decoding.”

This recent work by Qi and her colleagues enriches the present understanding of how the human brain executes more advanced motor tasks that require high levels of precision. In the future, their findings could inform the development of new BCIs that allow users to write on a computer via their brain signals.

“We are now focusing on the encoding and decoding model of more sophisticated fine movements, and our next goal is to construct BCIs capable of controlling diverse, sophisticated fine movements,” added Qi.

© 2025 Science X Network

Compared to other animal species, humans can plan and execute highly sophisticated motor tasks, including the ability to write complex characters using their hands. While many past studies have tried to better understand the neural underpinnings of handwriting and other complex human motor capabilities, these have not yet been fully elucidated.

Past studies showed that the motor cortex plays a crucial role in the human ability to translate intentions into actions. Yet the processes via which it enables the execution of precise and sequential movements, such as those associated with handwriting, are poorly understood.

Researchers at Zhejiang University in China recently carried out a study aimed at further exploring the role of the human motor cortex in the encoding of intricate handwriting, such as Chinese characters. Their findings, published in Nature Human Behavior, suggest that this encoding unfolds via a sequence of stable neural states.

“When we started this project in 2022, we initially aimed to construct a Chinese handwriting brain-computer interface (BCI) system,” Yu Qi, first author of the paper, told Medical Xpress. “Although our BCI system could decode the movement velocity well for cursor control, the reconstruction of Chinese handwriting was far from satisfactory.

“We were frustrated at the time, because we realized that handwriting is a skilled, sophisticated movement, especially for Chinese characters (> 3,500 frequent characters composed by 32 types of strokes), thus it could be more difficult compared with cursor control.”

Despite their initially disappointing results, Qi and her colleagues continued working on their handwriting BCI, by exploring the processes via which the brain, specifically the motor cortex encodes complex handwriting. Ultimately, their efforts yielded interesting new insight that allowed them to improve their handwriting human-machine interface.

As part of their recent study, they carried out a series of experiments in which a human participant was asked to write 306 distinct Chinese characters. While the participant was completing the task, the researchers recorded activity in his motor cortex, to understand the processes involved in the encoding of the characters and execution of the handwriting task.

“The experiment task was simple,” explained Qi. “We asked our participant to write Chinese characters one by one, with the guidance of a video, just like a karaoke game for writing. We recorded single-unit MC neural activity from his motor cortex (MC) with two microelectrode arrays as he performed the handwriting task.”

The brain activity recordings gathered by the researchers provided new interesting insights into the underpinnings of handwriting, suggesting that the motor cortex encodes complex handwriting by breaking it into a series of small movement segments or states. In their paper, Qi and her colleagues hypothesize that these states are the primitive units of movement encoding.

“The movement encoding in MC showed a state-dependent property that the encoding function remains stable within each state while varying largely across states,” said Qi. “Traditional neural decoders did not consider this state-dependent neural property such that failed to decode handwriting trajectories.

“These findings then inspired us to propose the state-dependent encoding and decoding model, along with the development of computational tools such as TFC and the state-dependent decoder, which enabled online Chinese handwriting decoding.”

This recent work by Qi and her colleagues enriches the present understanding of how the human brain executes more advanced motor tasks that require high levels of precision. In the future, their findings could inform the development of new BCIs that allow users to write on a computer via their brain signals.

“We are now focusing on the encoding and decoding model of more sophisticated fine movements, and our next goal is to construct BCIs capable of controlling diverse, sophisticated fine movements,” added Qi.

© 2025 Science X Network