[datensicherheit.de, 09|25|2025] Prof. Dr. Thorsten Zander, holder of the “Lichtenberg Professorship for Neuroadaptive Human-Technology Interaction“ at the Brandenburg University of Technology Cottbus-Senftenberg (BTU), has been researching the development of neuroadaptive technologies for more than 15 years now. To make his research findings ready for everyday use, he founded Zander Labs in 2016, which, unlike Elon Musk’s “Neuralink“, takes a non-invasive approach: The company develops passive brain-computer interface technologies. These can also be used to train AI (Artificial Intelligence) and ML (Machine Learning) models. At “brAIn@NAT25“, the first European conference for Neuroadaptive Technology and Artificial Intelligence, ds-editor Carsten J. Pinnow met with Professor Zander and asked him about the promising applications of this new technology:

photo: Dirk Pinnow

Prof. Dr. Thorsten Zander (l.) talking to ds-editor. Carsten J. Pinnow (r.)

Neuroadaptive Technology – control through the power of thought

Pinnow: Dear Professor Zander, many of our readers will probably not know what Neuroadaptive Technology is or what it means. Can you give us a brief insight?

Zander: Of course, of course! For over three decades now, research has been conducted into the development of interfaces for interaction between the human brain and a computer – so-called brain computer interfaces (BCIs). Brain activity is measured using electroencephalography (EEG), the data obtained is read out, analysed and then correlated with a specific cognitive state.

• For example: You think about moving your hand, for example to the left and right, while moving it to the left and right. If you record this data using EEG, feed it into a computer and connect it to a human brain via a BCI, the thought of ‘left’ or ‘right’ is enough to move a cursor left or right on the computer screen – without having to move a mouse or press a button, controlled purely by the power of thought.

pBCI: Addressing emotions and putting them to meaningful use

Pinnow: Very interesting! And where exactly does your new approach of a passive Brain-Computer Interface (pBCI) come into play?

Zander: Quite simply! Operating a keyboard or mouse by hand is easy and intuitive for most of us. The added value of a BCI for the consumer – if they are not physically impaired – is therefore rather low. But what if, instead of an active state of consciousness, we focus on a passive state of consciousness – feelings such as fear and joy, tension, calmness and so on – and try to make them usable?

• When we communicate with another person, we are constantly interpreting the facial expressions of our counterpart and then using our knowledge to make sense of the interaction. We understand what they are feeling and then automatically apply this knowledge in our interaction with the other person. A computer cannot do this – yet. pBCI technology opens the possibility of equipping computers with the necessary tools. They will be able to understand that we are reacting to a certain situation and interpret how we are doing so – to perceive our facial expressions without seeing us, so to speak. 15 years of research have shown me that the adaptation of a computer to a user can be noticeably improved using pBCI data. 
• For example, it can be ensured that a user always receives the right amount of information – not too much and not too little – so that they are never over- or under-challenged. Another possible application is optimizing the training of AI models on what really interests a user and what does not. AI can be enabled via pBCI to understand how users perceive the world around them – and then interact with them in a completely new, more user-friendly way. This is what we at Zander Labs would call Neuroadaptive AI.

Practical pBCI use cases

Pinnow: Can you illustrate this concept for our readers with some practical use cases?

Zander: Of course – no problem! Let’s say you want to learn a new language. If you do it with a computer programme, it’s sometimes too easy and you get bored. Or it’s too difficult, too fast, and you lose touch, think you’ll never make it and give up. If your language training programme had real-time data on the mental state of your brain, it could understand at what point you were surprised and overworked or bored – and increase or decrease the workload accordingly in real time. As a result, you could learn at exactly the right speed for you – and thus achieve significantly better learning results.

• Another use case could be text input on smartphones. You are probably familiar with this. You type in a word and then when you check it, it’s not the word you wanted to type. Autocorrect has simply changed it without your permission. For example, you wanted to type ‘Peter’ and now it says ‘Petra’. Correcting this repeatedly takes a lot of time and is frustrating. In such a situation, your brain sends out a clear signal, and we can harness this for you with the help of pBCI by, for example, forwarding it to the text program, which would then automatically ask you whether it should reverse the autocorrect or whether you would like to enter a different word directly. 
• pBCI could also be a real help when driving. Let’s say you are driving your car and witness an accident. You feel tense, stressed and inattentive to other traffic. Your thoughts are on the accident. Thanks to pBCI, your car computer could then register all of this and, to reduce distraction and stress, block incoming phone calls and reduce the music volume to allow you to concentrate on driving.

pBCI technology for training AI and ML models

Pinnow: Very interesting! You already mentioned that pBCI technology can also help with the training of AI and ML models. Can you explain this in more detail?

Zander: Let’s look at how Tesla trains its autopilot or how OpenAI does it with ‘ChatGPT’. Human feedback is always required for training. You need human experts to tell the models what they did right and what they did wrong. At Tesla, for example, these experts sit in front of 360-degree displays, watch a ride in slow motion and then manually tell the models what happened, whether the model’s reaction was okay or not. A lot of working time and manpower is needed for this.

• Training could be handled much more easily, quickly, and accurately by using pBCI to replace ‘right’ and ‘wrong’ with ‘I like’ and ‘I don’t like’. If we implement the technology in a training program, an AI can learn much faster – and more accurately – than would ever be possible manually.

Competitive situation in the market for Neuroadaptive Technology

Pinnow: And what about the competition? Is the market for Neuroadaptive Technology already highly competitive? When I think of BCI, ‘Neuralink’ comes to mind. How is Zander Labs positioned here?

Zander: As the technology is still relatively new and the market is only just being developed, the competitive situation is not yet very pronounced. Everyone is still researching and developing alongside each other – including ‘Neuralink’. But what makes us strikingly different is our approach. They focus on invasive procedures; we focus on non-invasive procedures. ‘Neuralink’ aims to implement electrodes at certain points in the human brain, which can then record very specific signals to use traditional, active BCI.

• But that is not what we want! We want to help passive BCI technology achieve a breakthrough. There are still smaller ‘competitors’ here, such as Emotiv and Urable, but unlike Zander Labs, they have very specific use cases. They will certainly be successful there too. I am convinced of that. But Zander Labs‘ approach is much more universal than theirs. We won’t get in each other’s way there.

pBCI as a success factor for Germany and Europe in the race for modern AI technologies

Pinnow: And what are the next steps for Zander Labs? What are you currently working on?

Zander: Our current focus is on translational research and development. We are working on ways to further optimize our pBCI technology so that it can dock better with other technologies and we can use brain signals even better for AI training. This will be our focus in the coming years.

Pinnow: And finally, where do you see pBCI in the long term? What opportunities and possibilities do you see?

Zander: I firmly believe that pBCI offers us a unique opportunity in Europe. We can develop AI here that simply cannot be developed in the USA and China because we are already more than one step ahead of them here in Europe.

• pBCI is a unique technology that has what it takes to take Germany and Europe one decisive step forward in the race for modern AI technologies.

Pinnow: Dear Professor Zander, thank you very much for this informative interview!

Further information on this topic:

btu Brandenburgische Technische Universität Cottbus-Senftenberg
Faculty 1 / Chair of Lichtenbergprofessorship Neuroadaptive Human-Computer Interaction / Prof. Dr. rer. nat. Thorsten O. Zander

btu Brandenburgische Technische Universität Cottbus-Senftenberg
Faculty 1 / Chair of Lichtenbergprofessorship Neuroadaptive Human-Computer Interaction / Head of the chair: Prof. Dr. rer. nat. Thorsten O. Zander / Lichtenbergprofessur

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